capacity and performance

Capacity and Traffic Performance of UnsignalizedIntersections under Mixed Traffic Conditions

Dissertation

zurErlangung des Grades eines

Doktor-Ingenieursder Fakultät für Bauingenieurwesen

an der Ruhr–Universität Bochum

vonJoewono Prasetijo, M.Sc.aus Pontianak, Indonesien

Bochum, August 2007

Diese Arbeit wurde von derFakultät für Bauingenieurwesender Ruhr–Universität Bochum alsDissertation angenommen und genehmigt

Dissertation eingereicht am : 15. Mai 2007Tag der mündlichen Prüfung : 16. Juli 2007

Referent : Prof. Dr.-Ing. Werner Brilon Koreferent : Prof. Dr. Henk van Zuylen, Transport Research Centre Delft (TU Delft)

PD Dr.-Ing. habil. Ning Wu

Preface

Unsignalized intersections are a key element in urban streets and in rural road networks. Themethodology for the analysis of such intersections as it has been established for the developedcountries fails when it should be applied for cities in developing countries due to the wide mixof vehicles with rather different characteristics and due to road user behavior. There arealready several attempts to develop alternative approaches for the analysis of unsignalizedintersections under mixed traffic conditions.

Dr.-Ing. Joewono Prasetijo has contributed new ideas to solve this problem in his dissertation.Basically the investigation is empirically oriented. However, the idea to describe trafficdemand by concentration of vehicles expressed by covered area within the intersection’sground space is rather innovative. This concept was able to express relations between quantityof traffic and performance of operation. On this basis Dr. Prasetijo is able to develop a newand successful procedure to quantify the level of service at unsignalized intersections undermixed traffic conditions.

The investigations have been conducted under the sponsorship of Technological andProfessional Skills Development Sector Project (ADB Loan No. 1792-INO), DeutscherAkademischer Austausch Dients (DAAD) and Lehrstuhl für Verkehrswesen, Ruhr-UniversitätBochum.

Bochum, August 2007 Prof. Dr.-Ing. W. Brilon

Contents

i

Contents

1 Introduction ...................................................................................................................... 11.1 Introduction................................................................................................................... 11.2 Problem Definition........................................................................................................ 21.3 The Objectives of the Study.......................................................................................... 51.4 Research Methodology ................................................................................................. 5

2 Transport Mode Hierarchy and the Existing Computation Method .......................... 72.1 Introduction................................................................................................................... 72.2 Transport Mode and Mode Characteristics................................................................... 72.3 Characteristics of Heterogeneous Traffic Flow ............................................................ 92.4 Current Capacity Measurement Based on Empirical Approach ................................. 15

2.4.1 Typical Free-Flow Speed Performance ............................................................ 152.4.2 Typical Urban Road Capacity Measurement under Mixed Traffic Flow ......... 172.4.3 Typical Conflicts at Unsignalized Intersections ............................................... 182.4.4 Typical Geometric Design Standard and Its Adjustment Factor ...................... 202.4.5 Adjustment Factor for Traffic Flow Performances........................................... 252.4.6 Adjustment Factor for Intersections’ Environment (”Side Friction”) .............. 272.4.7 Total Capacity of Unsignalized Intersections under Mixed Traffic Flow ........ 28

2.5 Conclusions................................................................................................................. 31

3 Capacity Computations Based on Rule of Priority Method....................................... 323.1 Introduction................................................................................................................. 323.2 Nearside/Offside Priority Intersection ....................................................................... 333.3 Capacity of ”Priority-to-the-Right” Intersection ....................................................... 363.4 Capacity Measured by Saturation Flow of Streams.................................................... 373.5 Capacity of All-Way Stop-Controlled and First-In-First-Out Intersections ............... 40

3.5.1 Departure Mechanism at AWSC/FIFO Intersections ....................................... 413.5.2 Capacity of A Stream in Several Departure Sequences.................................... 41

3.6 Conclusions................................................................................................................. 43

4 Field Measurement and Data Performance................................................................. 444.1 Introduction................................................................................................................. 444.2 Location of Field Measurement .................................................................................. 444.3 Equipment for Monitoring .......................................................................................... 474.4 Vehicle Classifications and Compositions.................................................................. 484.5 Passenger Car Units (PCUs) ....................................................................................... 52

4.5.1 Measurement Methods of Passenger Car Units ................................................ 534.5.2 Measurement at Passenger Car Units Under Mixed Traffic Flow.................... 544.5.3 Passenger Car Units Measured by Projected Rectangular Area of Vehicles.... 60

4.6 Traffic Flow Performance........................................................................................... 724.7 Mean Speed Performance ........................................................................................... 774.8 Intersection Occupancy (percent of intersection area)................................................ 854.9 Conclusions................................................................................................................. 88

5 New Approach of Capacity Calculation Based on Conflicting Streams ................... 905.1 Introduction................................................................................................................. 905.2 Conflicting Stream Description and Conflict Group .................................................. 905.3 Speed and Flow Performance of Conflict Groups ...................................................... 985.4 Speed of Each Stream and the Total Flow of Conflict Group ................................. 1005.5 Speed and Flow Relationship for Each Type of Vehicle Stream.............................. 102

Contents

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5.6 Speed and Flow Relationship of Each Stream.......................................................... 1055.7 Speed - Flow and Flow - Intersection Occupancy Relationship............................... 1095.8 Capacity Defined by Speed and Flow of Conflict Streams ...................................... 1175.9 Capacity Analysis for Three-Leg Unsignalized Intersections .................................. 1255.10 Conclusions............................................................................................................... 144

6 Traffic Quality and Performance ............................................................................... 1456.1 Introduction............................................................................................................... 1456.2 Maximum Flow (Capacity) from the conflict streams method................................. 1456.3 Capacity Calibrated................................................................................................... 1486.4 Relationship Between Speed and Flow of Intersection ............................................ 1516.5 Relationship Between Flow and Intersection Occupancy of Intersection ................ 1526.6 Delay and Probability of Queue................................................................................ 155

6.6.1 Delay ............................................................................................................... 1556.6.2 Probability of Queue . ..................................................................................... 163

6.7 Conclusions............................................................................................................... 167

7 Pedestrians’ Behaviors................................................................................................. 168

8 Conclusions and Recommendations ........................................................................... 171

9 References ..................................................................................................................... 173

Appendix A : Geometric Design and Traffic Flow Performance

Appendix B : Traffic Flow Composition

Appendix C : Mean Speed and Passenger Car Units of Each Traffic Stream

Appendix D : Matrix of Maximum Flow (Capacity)

1 Introduction

1

1 Introduction

1.1 Introduction

Capacity at unsignalized intersections is measured by various approaches which can becharacterized as deterministic and probabilistic. The first method is the gap acceptanceprocedure (GAP), which was developed in Germany (GRABE, 1954; HARDERS, 1968) andwhich is also used in the United States and in several European Countries. The basic principleof this method is to calculate the capacity at unsignalized intersections based on so–calledcritical gaps and follow–up times for the vehicles from the minor road. The second method isthe empirical regression technique. Its application is mainly based on research from theUnited Kingdom (KIMBER, COOMBE, 1980). The method is based on a large number offield data in modern British streets by the use of regression functions. This approach ofcapacity estimation is also expanded by the consideration of road geometric design, visibilitydistances, demand flows, turning proportions and vehicle types.

The third method in calculating the capacity at unsignalized intersections is the conflicttechnique. This new approach is based on the method ”Addition of critical movement flows”(GLEUE, 1972). The theory has first been developed by WU (1999) for the Americansolution of All–Way Stop–Controlled (AWSC) intersections in such a way that the First–In–First–Out discipline applies. The model considers all possible traffic streams and conflictpoints at intersections simultaneously. The interaction and impact between flows at theintersection is formulated by a mathematical approach. This procedure can also imply flowsof pedestrians and cyclists crossing the intersection. This method has been used successfullyfor calculating capacity at unsignalized intersection (BRILON, MILTNER, 2005).

Up to now the estimation of intersection capacity is standardized in Indonesia by theINDONESIAN HIGHWAY CAPACITY MANUAL (1997). This manual was developed bythe analysis of a large number of traffic data from 147 roads in 16 cities in Indonesia.Investigations have been made within 6 years (1991 – 1996) by the Swedish Road and TrafficResearch Institute (SWEROAD). The manual is based on empirical analysis. Traffic behaviorpatterns at unsignalized intersections in Indonesia is completely different from that ofdeveloped countries. For example rules of priority are almost completely neglected. Also,drivers become more aggressive when they approach the intersections and no lane disciplineapplies. Therefore, all highway capacity guidelines from developed countries cannot beapplied successfully in Indonesia.

Traffic and transportation in developing countries are also very different to developedcountries since traffic composition and level of road side activities are in contrast todeveloped countries. Traffic rules, for examples, like give way or lane discipline etc. are

1 Introduction

2

neglected in most cases. Drivers are more aggressive so that a gap acceptance behavior israther uncommon. In case of unsignalized intersections, almost two third (2/3) of vehicles didnot wait for a gap. If there is any critical gap which is likely to be accepted, then this is verysmall with about 2 seconds.

Vehicle types in developing countries show a large variability which makes traffic flow ratherheterogeneous. This traffic flow consists of transport modes of varying dynamiccharacteristics sharing the same road space. In this view, vehicles contribute to variation inspeed behavior ranging from slow vehicles to rather fast–moving cars. Typical for developingcountries, is there is also a great number of activities occurring at the edge of the road, bothon the roadway and shoulders and sidewalks. Most of these activities create numbers ofconflicts called ”side friction”. The Indonesian manual gave much attention to such aspectslike ”side frictions” which have great impact on capacity and performance are pedestrians,stops by transport vehicles and parking maneuvers, motor vehicles entries and exits into andout of roadside properties and side roads, and slow–moving vehicles (bicycles, rickshaw,etc.). Side friction is measured qualitatively with respect to traffic engineering considerationas high, medium and low.

Typical cities of developing countries are characterized by heterogeneous traffic (mix of non–motorized and motorized modes) and mixed land–use patterns. Non–motorized modes areowned and used by a large number of people. Motor vehicle ownership in Asian countries islow compared to North America and European countries. In 1993 a figure of 29 cars per 1000residents in East Asian countries was counted, which could be compared with 560 cars per1000 residents in North America, or 366 in OECD countries. Although the growth rate ofmotorized vehicles in Asian cities is large, most of these increasing numbers of vehiclesconcern motorized two–wheelers and three–wheelers. These types of vehicles are estimated tobe more than 50 percent of all motor vehicles in Thailand, Malaysia, Indonesia and Taiwan.

1.2 Problem Definition

Most developing country cities have been classified as ”low cost strategy” cities. Incomparison with cities in the West, these cities consume less transport energy. Characteristicsof these urban centers are high density, mixed land use, short trip distances, and high share ofwalking and non–motorized transport. Modes of heterogeneous traffic flow in developingcountries consist of vehicles with varying dynamics and space requirements sharing the sameroad space. However, the concepts of the traffic flow theory in the United States, Europe, andAustralia are formulated for motorized four–wheel road traffic which constitutes ahomogeneous traffic flow. Traffic streams in heterogeneous traffic consist of distinctcategories of vehicles. The Indonesian Highway Administration distinguishes between 13classes of vehicles for its routine classified counts. Traffic, thus, consists of many motorizedtwo–wheelers, motorized three–wheelers, bicycles, non–motorized three–wheelers, cars,

1 Introduction

3

buses and pull carts. Furthermore, if there is a lack of adequate pedestrian facilities, trafficstreams may also contain a significant on–road pedestrian flow.

Much less attention is given in providing adequate and suitable facilities for non–motorizedvehicles and pedestrians. In order to solve the problems on urban streets we still concentratemore on the motorized transport. Non–motorized transport constitutes a significant share oftotal traffic in many Asian cities. In 1985, even in a city like Jakarta, non–motorized vehiclessuch as becaks (three–wheelers) and bicycles accounted for 4.6 percent and 2.4 percent,respectively, of total trips. For some cities like Yogyakarta and Bandung (Java), thisproportion is even higher. In the same year, Jakarta had a population of more than 3.5 millionand walking still accounted for 40 percent of total trips, whereas in Bandung (populationabout 1.5 million) and in Yogyakarta (population about 0.6 million in 1976) walking tripsaccounted for about 49 percent and 50 percent of total trips, respectively. It can be seen that inIndonesian cities, non–motorized modes still have an important function as a travel mode.However, due to a large number of speed differences between non–motorized slow–movingvehicles and motorized transport or fast–moving vehicles of about 45 km/h to 60 km/h, thiscould be a serious problem in traffic operation with regard to capacity and safety.

The current traffic behavior patterns in developing countries is also different to those ofdeveloped countries regarding unsignalized intersections. The common rules of ”give way”and ”priority from the left” are not fully respected in most cases. The intersections are oftenblocked by drivers trying to ”cut the corners” and they become more aggressive whileapproaching the intersections, especially when the degree of saturation is higher than 0.8 –0.9. Previous studies have shown that two–thirds of vehicles coming from minor roads crossthe intersection without waiting for gaps and critical gaps were found to be about 2 seconds.Gap acceptance behavior is very uncommon at unsignalized intersections in Indonesia.

The Indonesian manual has described the capacity under mixed traffic flow which is based onthree aspects

■ geometric conditions■ traffic situations■ road environment.

Standard design of intersections justifies the basic capacity which has values between 2700pcu/h and 3400 pcu/h with seven types of intersections. The proportion of traffic flowmovements, i.e. major left–turn, major right–turn and flows from the minor road are importantto select adjustment factors to get the real value of capacity. Road environment means thatseveral parameters such as city size, type of road, side friction and degree of non–motorizedtransport would have impacts on the capacity. Non–motorized vehicles are counted asmovements instead of side friction factors, and its passenger car unit values are assumed to bethe same as for light vehicles/cars with a value of 1.0. The value could be regarded as

1 Introduction

4

unrealistic when the proportion of non–motorized traffic flow at intersections is increasing,especially at times when the flow is really mixed.

With an empirical approach, interactions between movements flowing from different arms ofan intersection are not considered. Conflicts are normal and movement priority is notrespected. Significant studies for Two–Way Stop–Controlled (TWSC) intersections werealready carried out in Indonesia, but only a limited number of studies handling the trafficprocess at unsignalized All–Way Stop–Controlled intersections is available. Results fromthose studies could not find situations with varying conditions in the real world, because allstreams at AWSC intersections are considered to be equal in the hierarchy of the priority ofdeparture.

A new approach of capacity analysis is the so–called conflict technique. This method could bean alternative solution in order to analyze more complex interactions between streams atintersections, i.e. First–In–First–Out intersections which are very popular in developingcountries. Although, there are no significant procedures up to now for FIFO intersections,however, it can be treated in the same manner like AWSC because the departure priority issimilar at both types of intersection (WU, 1999). This technique is based on an idea of the”Addition of Conflict Flows” (ACF) (GLEUE, 1972). General forms of this method rely onthe occupation times of cars for one specific point of the intersection under certain conditions.For determining the capacity the occupation time of 3.6 seconds has been adapted for thecase of AWSC. Earlier studies for single–lane AWSC intersections found that turningmovements did not affect the occupation time significantly. Therefore, the capacity of astream can be represented as C0 = 3600/tB with departure headway, tB between 3.5 s/pc and4 s/pc.

Preliminary studies (PRASETIJO, 2005) at three intersections in Indonesia (secondary data)with the basic capacity, e.g. three–legs and four–legs intersections (2700 pcu/h – 3400 pcu/h)and some adjustment factors (IHCM, 1997) have shown that the average value of occupationtime is about 3 s/pc – 2 s/pc where non–motorized vehicles were assumed to have a PCU =1.0, which is the same as the light–vehicle (cars). Therefore, based on driver behavior, trafficcomposition and roadside activities, the real traffic situation at AWSC and FIFO intersectionsin Indonesia could differ from those in developed countries. This study will further investigatethe impact of these aspects in order to get a realistic picture of unsignalized intersectioncapacity.

1 Introduction

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1.3 The Objectives of the Study

The main objectives of the study are :

1. Investigate the traffic performance at unsignalized intersections under mixed trafficconditions, e.g. speed, flow and intersection occupancy.

2. Investigate parameters that can be used to describe maximum flow (capacity).

3. Develop new procedures of capacity measurement which take into account mixedtraffic flow at unsignalized intersections based on conflict streams.

4. Look on suitability of the method to measure the capacity compared to other methodsthat have been widely used in Indonesia.

5. Measure the traffic performance of unsignalized intersections based on assessmentfrom the new method and the Indonesian Manual (IHCM, 1997).

1.4 Research Methodology

In order to achieve the objectives of this research, literature review was performed regardingvarious methods to determine capacity and delays. Two different approaches are knownpreviously which were used an empirical and mathematical pragmatic approaches whileanother (IHCM, 1997) have counted the capacity by the main three aspects of trafficmovements, geometric design and environment. Traffic movements are not assessed to becapacity parameters directly instead of adjustment factors. Therefore, the real capacity is notcalculated by the real value of traffic flow where the real flows are the most importantassessments with regards to the interaction between the streams at the intersections.

The study has investigated 14 unsignalized intersections in the city of Pontianak, WestKalimantan and in a secondary set of data from Yogyakarta (West Java) in Indonesia. Therewere 10 three–leg unsignalized intersections which have been evaluated for the analysis dueto receiving an appropriate number of vehicles and various types of vehicles to represent themixed traffic flow characteristics. Both cities have very typical traffic flow characteristics,e.g. a heterogeneous traffic (motorized and unmotorized). Each of the intersections had atraffic performance and geometric design (different widths of legs) which are different fromeach of the other. Several aspects regarding traffic flow, intersection design, and roadenvironment must be considered, e.g. speed, traffic volumes at the minor road and at themajor road, geometric design of the intersections, roadside activities and type of environment(commercial, residential, limited access). These parameters were monitored by videocamcorders and were manually extracted from the videos. The selected sites of unsignalizedintersections are designed on the background of the standard manual for geometric designpublished by Ministry of Public Works. Investigations have only been made at three–legintersections.

1 Introduction

6

The intersections were chosen among places where a rule of priority is not really existent andwhere all streams seem to have an equal rank in the hierarchy of departure mechanism. Only asmall number of vehicles is expected to stop since the capacity has not been reached at any ofthe study sites. Every stream was observed by using two camcorders (DCR–TRV 270E withadditional cassette Hi8) which were placed at a 3.5 meter high tripod and each was positionedat the edge of the road near the corners of the intersection. From these points the trafficmovements could be observed very clearly. Each intersection was investigated during twohours in the morning (06.30 – 08.30) and in the afternoon (14.30 – 16.30). These periods wereconsidered as the peak period times (MINISTRY OF PUBLIC WORKS, 1998).

Data were counted from the recorded cassettes by using a special time–code machine and bymonitors. First, data from the recorded Hi8 cassettes had to be transferred to the VHS (VideoHome System) video cassettes in order to get the time–code (The time–code recorder can onlyoperate VHS video cassettes). Viewing the monitor, time instants when the vehicles arrive atspecified points of the intersections were transferred into a personal computer using a specificsoftware. Times of arrival and departure were recorded for each vehicle from each stream.That means: each VHS video cassette had to be observed and evaluated more than six times,because each intersection (three–legs) has six movements. The intersection occupancy wasmeasured in a different way (number of vehicles that occupied the intersection area at thecertain time). Based on the arrival and departure time as well as the traveled distance of eachmovement, we can simply find the speed of each vehicle. Traveled distance for eachmovement were measured based on reference lines which were drawn at the intersections andwhich could also be seen at the monitor (cassette recorded). Furthermore, speed and volumeswere aggregated in 1–minute and 5–minute intervals. In addition, intersection occupancy wascounted in 20–second intervals.

2 Transport Mode and the Existing Computation Method

7


2.1 Introduction

The concept of a road hierarchy is a familiar one in the field of traffic engineering and trafficmanagement. In Indonesian cities, reasonably clear road hierarchies exist, although use doesnot always correspond to function (SOEGIJOKO, HORTHY, 1991). In Indonesia, the roadhierarchies consist of the following classes :

1. Primary arteries (intercity roads passing through the city, with road widths usuallymore than 8.0 m)

2. Secondary arteries (main roads linking major activity centers in the city, includingthe central business district of widths about 7.0 m)

3. Secondary collectors (roads connecting the secondary arteries with the residential

areas or other urban activity locations, of widths between 6.0 m and 8.0 m)

4. Local roads (of width between 4.0 m and 6.0 m) and roads within a communityconsisting of narrow paved streets (width between 2.5 m and 4.0 m) and pavedand unpaved footpaths, often inaccessible to four–wheeled motorized transport (ofwidths between 1.0 m and 2.0 m).

Conversely, urban transportation demands arise from hierarchies of activities taking place in ahierarchy of urban communities as known in Indonesian cities: the Kecamatan (about 20,000households), the Kelurahan (5,000 households), the Rukun Warga (250 households), and thesmallest community, the Rukun Tetangga (25 households). The location of these activitiesgenerally determines trip distances. Average destinations of frequent trips are closer to theresidential areas and those less frequently visited are further away, the demand for shortertrips is generally much greater than it is for long trips. Most of the short trips are at the bottomof the speed hierarchy and at the lower levels of the road hierarchy, for maximum efficiencythe modal mix should be different at different levels of the road hierarchy, as certain modesare more appropriate than others for certain trip lengths. Instead of that, number of transportmodes do not always rely on road hierarchies, because even for long trips, people are likely todrive/ride lower speed vehicles/mode transport (e.g. bicycles, motorcycles). Within thissituation, traffic conditions in Indonesia differ markedly from those encountered in developedcountries.

2.2 Transport Mode and Mode Characteristics

In order to give an overview of common Indonesian transport modes, a representativemember of each of the main categories has been selected as follows

■ Private Transport Modes


8

1. Pedestrian2. Pedestrian with push cart (gerobak or kakilima)3. Bicycle4. Motorcycle5. Automobile

■ Public Transport Modes1. Becak – a three–wheeled, non–motorized pedicab specific to Indonesia;2. Andong – a four–wheeled horse–drawn carriage in a number of varieties, such

as the two–wheeled dokar;3. Ojek – a motorcycle for hire in the manner of a taxi;4. Bajaj – a motorized tricycle using a 150-cc scooter engine, steered like a

scooter;5. Bemo – a three–wheeler with 360-cc engine;6. Mikrolet I – a motorized vehicle using a 1000-cc engine;7. Mikrolet II – a motorized vehicle using a 1500-cc engine;8. Minibus – a small bus using a 3300-cc engine;9. City bus – a Mercedes model using a 5675-cc engine.

The Indonesia Highway Administration distinguishes between 13 classes of vehicles for itsroutine classified counts. In the Indonesian Highway Capacity Manual (1997), the followingseven (7) classes were distinguished as :

■ Light vehicles (LVs) : passenger cars, jeeps, minibuses, pickups, microtrucks;■ Medium heavy vehicles (MHVs) : two–axle trucks with double wheels on the rear axle, buses shorter than 8 m;■ Large trucks (LTs) : three–axle trucks;■ Truck combinations (TCs) : truck plus full trailer, articulated vehicle;■ Large buses (LBs) : buses longer than 8 m;■ Motorcycles (MCs);■ Unmotorized vehicles (UMs) : tricycles and bicycles.

From most of the resources, the modes can be arranged in four speed bands, as follows :1. (about 5 km/h) : Pedestrian

Pushcart2. (10 to 20 km/h) : Bicycle

Becak3. (25 to 40 km/h) : Bemo

Bajaj Motorcycle (low–power)

4. (50 to 100 km/h) : Mikrolet Minibus City bus Motorcar


9

Due to the obstruction per passenger at different road widths and traffic velocity, theutilization of road space could be one of a transport modes’ efficiency. The road utilizationefficiency of a transport mode makes an important contribution to its true economic cost. Thisroad utilization function, RUF depends on more than the planned area of a transport mode; itis a function of vehicle length and width, width of the road, modes’ maximum speed, free–flow speed of the surrounding traffic, number of passengers, average distance between stops,and possibly other factors as well. The following Table 2-1 shows the characteristics oftransport modes in Indonesia. Further, the static characteristics (length and width) anddynamic characteristics (average speed) of vehicles become an important parameter in orderto analyze traffic streams behavior at intersections relating to the capacity analysis.

TransportModes

Length[m]

Width[m]

OfficialPassengerCapacity[person]

ExtraFreight

Capacity[kg]

CruisingSpeed[km/h]

AverageSpeed[km/h]

IdealTrip

Length[km]

AverageTrip

Length[km]

PrivatePedestrian 1.00 0.60 1 30 5 3.5 0.4 1.1PedestrianandPushcart

2.10 0.80 1 200 5 - 0.4 -

Bicycle 1.75 0.60 1/2 50 16 6.0 3.3 2.8Motorbike 1.60 0.80 2 30 80 - - -Automobile 4.05 1.60 5 100 100 - - -

PublicBecak 2.25 1.00 3 30 10 5.3 1.5 2.3Andong 3.50 1.50 7 100 10 - 1.6 -Ojek 1.60 0.80 2 15 60 - - -Bajaj 2.50 1.20 3 30 40 - - -Bemo 2.90 1.25 8 30 40 - - -Mikrolet I 3.80 1.80 10 70 60 - - -Mikrolet II 4.25 1.25 11 80 60 - - -Minibuses 5.40 1.90 26 - 60 - - -City Bus 9.30 2.50 51 - 60 - - -

Table 2-1.Transport Mode Characteristics (SOEGIJOKO et al., 1991)

2.3 Characteristics of Heterogeneous Traffic FlowMost of the developing countries, e.g. Indonesia, have different traffic situations from those ofdeveloped countries. They have a large number of differences in drivers’ behavior, trafficcomposition and level of roadside activities. In general, the traffic stream consists of twodistinct categories of vehicles, namely fast–moving (motorized) vehicles and slow–moving(non–motorized) vehicles. The static and dynamic characteristics of these two types ofvehicles vary widely. A vehicle from any approach can enter the intersection area only whenthe sum of its required crossing time and its arrival time is lowest, and when all theconflicting vehicles are simultaneously considered.


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Most studies analyze mainly homogeneous traffic with a low percentage of slow–movingvehicles. The models mainly belong to the powered–vehicle group. In developing countriesthe traffic scene is altogether different, with a large volume of motorized two–wheelers orthree–wheeler auto–rickshaws and other slow–moving vehicles. There is mixed traffic,consisting of power, manual (and animal) – driven vehicles whose physical and operationalcharacteristics vary considerably. Therefore, vehicle–arrival characteristics at urbanuncontrolled intersections with mixed traffic conditions are rather complex and need specialattention.

The static and dynamic characteristics of slow–moving vehicles and fast–moving vehicles(motorized) vary widely/considerably and this will cause disturbances of traffic operation andreduces capacity of the road. It is difficult to estimate the traffic volume and capacity of afacility under mixed traffic flow unless different vehicle classes are converted to a commonunit, e.q. passenger car units (PCUs). These units, PCUs for each type of vehicle are designedwith consideration of static and dynamic characteristics of vehicle. It correlates of flow ratesof passenger cars only with that of mixed traffic streams that are equivalent in terms of thedrivers’ perception of the Level of Service (LOS).

The impacts of NMT on traffic flow have been traditionally assessed by converting NMTtraffic into passenger car units (PCUs), also known as passenger car equivalents (PCEs),which are then added to the PCUs for motorized traffic. This approach assumes that MT andNMT form a combined stream of ”mixed traffic”, whose PCUs–based relationship may bemeaningfully analyzed. But it is widely accepted that PCUs values change depending ontraffic composition, number of lanes, and the degree and length of gradients. Therefore, it isdifficult to obtain standard values applicable across different road and traffic conditions.Another approach takes the view that MT and NMT do not really mix because of difference intheir cruising speeds and other operational and physical characteristics, instead, this approachendeavors to model the impacts of NMT on MT flows by treating two streams separately. Theflows at the various critical points in terms of the NMT flow and speeds are

NMT

VEL

ELNMTQ 3600

=

whereNMTVEL = Average velocity of the NMT [m/s]ELNMT = Effective length/minimum length of the NMT [m]

The effective capacity approach considers technical relationships concerning NMT and MTinteraction, and is convenient because the accumulated knowledge regarding capacity andspeed – flow relationships can effectively be incorporated into the analysis. The drawbacks ofthe ”effective capacity” approach have given rise to an aggregate approach by using ”friction

(2-1)


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factor” or ”side friction”. The concept of ”friction” is used to describe the degree of NMTimpacts on the speed and capacity on motorized traffic. Various roadside activities arecombined into a single speed reduction factor, such as :

1. Pedestrian (walking and crossing)2. Bicycles3. Non–Motorized4. Slow–moving vehicles5. Roadside vendors6. Stops of public transport/ bus7. Parking maneuver8. Vehicles exits and enter the roadways

By using the concept from HOBAN (1987), previous field studies and researches inIndonesia (SWEROAD, 1994) have measured friction effects on speed. This conceptidentified four significant friction items :

1. Pedestrian movements2. Stopping public transport vehicles3. Parking activities4. Vehicles entering and leaving roadside premises

Heterogeneous traffic flow consists of modes of varying dynamic and static characteristicssharing the same road space. Underlying concepts of the traffic flow theory in the UnitedStates of America, Europe, and Australia are formed by motorized four–wheel road trafficdominating in those areas, i.e., homogeneous traffic. All car following, lane changing logicand systems’ measure of effectiveness used in microscopic simulation programs ultimatelyuse field data from these countries for calibration.

For heterogeneous traffic, having an ideal capacity per lane is mis–conceptual because lanediscipline is very loose. Vehicles have varying static and dynamic characteristics. These sharethe same road space and move by sharing the lateral as well as the linear gaps. For example, amotorcycle rider judges whether the lateral distance (width) between a motorcycle and bus isacceptable to progress on the roadway. Another motorcycle rider in the same situation wouldhave a different critical width acceptance. If the width is unacceptable, then an entity isconstrained by preceding entities. Critical width acceptance depends on three items. First, thetravel speed of the vehicle/entity itself. Second, the physical width of the vehicle anddistribution of the width acceptances of specific entity groups, i.e., driver/rider/pedestrianbehavior. Each vehicle/entity group has its own critical width acceptance.

Heterogeneous traffic can have many motorized two–wheelers, motorized three–wheelers,bicycles, non–motorized three–wheelers, cars, buses, trucks, animal–drawn carts, push andpull–carts. Additionally, if sidewalk facilities are inadequate or lacking, this diverse mixture


12

contains significant on–road pedestrian traffic. In homogenous traffic, traffic entities formone–dimensional queues develop; in heterogeneous traffic, mass queues develop. Thesequeues are built lengthwise as well as laterally, see Figure 2-1 and Figure 2-2.

Figure 2-1. Homogeneous Traffic with One Figure 2-2. Heterogeneous Traffic with Two Dimensional Queue Dimensional Queues

The ”car following” notion used in homogenous traffic flow models is not applicable inheterogeneous traffic. Since cars do not comprise most of the traffic mixture, ”car following”is an incorrect term for heterogeneous traffic. Furthermore, since width of entities vary greatlyin heterogeneous traffic, figuring out which leading entity/vehicle it is following is difficult.Leading entities may run parallel or in a staggered way, see at Figure 2-3 and Figure 2-4.

Figure 2-3. Homogeneous Traffic has Lane Discipline

Figure 2-4. Heterogeneous Traffic with Parallel/Staggered Entity – Following

In ”the lane changing” notion, heterogeneous traffic has been derived in some extensivemodels and algorithms. Microscopic studies of this traffic show that the time headwaybetween vehicles is an important flow characteristic that affects safety, level of service,driver’s behavior and capacity of transportation system. A minimum time headway mustalways be present to provide safety in the event that the lead vehicle suddenly decelerates.

One dimensional queues

Traffic flow

{ Two dimensional queues

Traffic flow

{

Traffic FlowLane discipline

Traffic FlowParallel/ staggered


13

The percentage of time that the following vehicle must follow the vehicle ahead is oneindication of level or quality of service. The distribution of time headways determines therequirement and the opportunity for passing, merging, and crossing. The capacity of thesystem is governed primarily by the minimum time headway and the time headwaydistribution under capacity – flow conditions.

Figure 2-5. Homogeneous Traffic Uses Lane Concept

Figure 2-6. Heterogeneous Traffic Uses Width Acceptance/Entity Envelope

Lane discipline is deficient in heterogeneous traffic not because driver behavior issignificantly different, but because heterogeneous traffic consists of entities of various widthsand varying dynamic characteristics (TIWARI, 2001), see also Figure 2-6. With small widthdifference in homogenous traffic (Figure 2-5), drivers find it optimal and advantageous toadopt lane discipline to transverse the roadway space giving the narrowness of the widthrange. However, in heterogeneous traffic whose range is approximately 6.0 m to 4.9 m,drivers and pedestrians find it optimal to advance by accepting lateral gaps (widths) betweenpreceding entities. Heterogeneous traffic uses road space more efficiently than homogenoustraffic. For this traffic, models based on width acceptance can ultimately produce a goodestimate of roadway capacity and assessments of operations and safety of various facilitydesigns.

Various road users have different and often conflicting requirements. Motorized vehicles needclear pavements and shoulders, while bicyclists and pedestrians need shaded trees along thepavement to protect them from the summer sun. Owners of private transport modes like MTWand automobiles prefer uninterrupted flow, fewer stops and minimum delays at intersections,whereas public transport buses require frequent stops for picking and discharging passengers.Motorized four–wheeled vehicles like cars, buses, etc., perform better if they move in queues

Traffic Flow

Lane concept

Traffic Flow

Width acceptance concept


14

with minimum braking and acceleration. Since our infrastructure design does not account forexisting conflicting requirements of different modes, all modes have to share the road spaceand operate in sub–optimal conditions.

In most of the cities in Indonesia, walking is the dominant mode of transport for work,shopping, and education trips. In larger cities such as Jakarta walking still accounts for 40percent of total trips (1985) and this proportion has not changed much in recent years. Next towalking, bicycles and pedicabs are other non–motorized modes in some cities in Indonesia.Although their use is limited, some parts of the population are still dependent on these modes.Examples of transport mode and destination at three medium–sized cities in Java can be seenin Table 2-2 and Table 2-3.

In 1985, even in a city like Jakarta, becaks and bicycles account for 4.6 percent and 2.4percent, respectively, of the total trips. For some cities like Yogyakarta and Bandung, thisproportion is even higher. For example, in Bandung in 1976, 9.7 percent and 5.8 percent ofthe total trips were made using becaks and bicycles, respectively. A study (MARLER, 1985)in some high–density communities in Bandung concluded that work trips using becaks areeven more frequent, about 12 percent of the total trips and there was also evidence that the useof bacaks is more significant for non–work trip purposes.

Number of Private VehiclesCity Area

[HA]Population[Thousand] Bicycles Motorcycles Cars

Serang (S) 11.6 111.5 10,000 (1983) 3,000 246Tasikmalaya (T) 19.2 156.7 16,000 14,600 2,250Cirebon (C) 60.1 275.0 9,700 3,600 876

Table 2-2. Profile of Three Medium–Sized Cities in Java (SOEGIJOKO et al., 1991)

In Indonesian cities, from the medium–sized to the larger cities, including metropolitan citieslike Jakarta – non–motorized modes such as walking, bicycles, and pedicabs still have animportant function as a travel mode. Walking is especially important, perhaps because triplengths in these cities are short. In general, shopping trips are especially short trips, less than1 km. The factor might be the limitations of the motorized public transport service in terms ofarea coverage and services.


15

Work [%] Shopping [%] Education [%]City City CityVehicles/

ModeS*) T C S T C S T C

Walk 48 58 29 80 72 57 83 70 53Becak 9 8 6 4 3 1 3 4 7Bicycle 4 2 12 0 5 6 6 2 7Motorcycle 12 7 11 0 15 23 0 8 5Car 6 0 8 0 0 1 2 0 0Minibuses 21 25 34 16 5 12 6 16 28

Table 2-3. Trip Purpose and Transport Modes in Three Medium–Sized Cities (SOEGIJOKO & HORTHY, 1991)

*) S = Serang, T = Tasikmalaya, C = Cirebon

2.4 Current Capacity Measurement Based on Empirical Approach

Highway capacity manuals from developed countries cannot be applied successfully inIndonesia because of large differences in drivers’ behavior, traffic composition, and level ofroadside activities. Results from interim manuals from 1990 for urban traffic facilities,interurban roads, and superhighways have shown that the light–vehicle free–flow speed for aflat two–lane two–way road at ideal conditions is considerably lower in Indonesia than indeveloped countries, free–flow speed is reduced by roadway width and side friction such aspublic transit stops, pedestrians, non–motorized vehicles, and entries and exits from roadsideproperties and minor roads, Indonesian drivers tend to overtake at short sight distances, whichreduces the slope of the speed – flow curve, and the capacity for two–lane, two–way roads isslightly higher in Indonesia than in developed countries.

2.4.1 Typical Free–Flow Speed Performance

Based on investigation relate to IHCM report that free–speed was determined forunobstructed vehicles defined as vehicles with a headway to the nearest vehicle in front ofmore than 8 seconds and no recent or immediate meeting with a vehicle in the opposingdirection (± 5 seconds). The regression analysis was performed with travel time (TT) asdependent variable with the following equation :

Z.....DYCXBconst.V

TTLV

⋅+⋅+⋅+==1

whereVLV = Average speed of light vehicles [km/h]X,Y,Z,...... = Selected independent variables [-]B,C,D,...... = Regression coefficients. [-]

(2-2)


16

Typical free–speed of various types of vehicles based on the type of terrain at the certain roadsection in Indonesia were investigated and presented in Table 2-4. The following list of basefree–flow speed are based on several years of investigation (6 years) at several roads.

Base Free–Flow Speed [km/h]Type ofTerrain Light Vehicle Large Buses

MediumHeavy

VehicleLarge Trucks Motorcycle

Flat 68 73 61 58 55Rolling 61 62 52 49 53Hilly 55 50 42 38 51

Table 2-4. Base Free–flow Speed 2/2 UD Road (BANG et al., 1995)

In Indonesia often a great deal of capacity occurs at the edge of the road, both on the roadwayand on shoulders and sidewalks, which interacts with the flow of traffic, causing it to be moreturbulent and hurting capacity and performance. Equation 2-3 shows that free–flow speed ismainly affected by carriageway width, side friction and road functional class (arterial,collector or local). The following types of side friction events which were recorded manuallyin the IHCM field surveys were defined as :

■ PED : number of pedestrians, whether walking along or crossing.■ PSV : number of stopping by public transport vehicles (motorized and non– motorized) and parking maneuvers.■ EEV : number of motor vehicle entries, exits into and out of roadside properties.■ SMV : slow–moving vehicles (bicycles, trishaws, etc.)

The actual free–flow speed for each vehicle type can be calculated in IHCM (1997) asfollows :

RCSFW FFVFFV)FFV(FVFV ⋅⋅+= 0

where

FV = Free–flow speed for actual conditions [km/h]FV0 = Base free–flow speed for predetermined ideal conditions [km/h]FFVW = Adjustment for effective carriageway width [-]FFVSF = Adjustment for side friction [-]FFVRC = Adjustment for road functional class and land use [-]

Previous studies on speed and flow relationship on roads have been done at 5–minute periodobservations with pre–determined flow classes 0 – 300, 301 – 600, ... (lvu/h) for several sitesin Indonesia (IHCM, 1997). The impact of site conditions (carriageway width, side friction,land use, road function class, sight distance class) were analyzed with multiple regressions.Speed – flow regressions were made for each road class (e.g. 2/2 UD carriageway width 6.5 m– 7.5 m) with the following linear speed – flow model which would have R2 > 0.6 (Figure2-7) and there was no apparent knee in the relationship. Similar linear relationships were

(2-3)


17

obtained for each vehicle type, with the lines converging at a speed of 35 km/h – 40 km/h at aflow level of 2,900 lvu/h. In this sense, it is difficult to have an ideal relationship betweenflow and speed which follow the relationship as in the fundamental diagram of traffic flow.

Figure 2-7. Speed – Flow Relationship for Light Vehicles Undivided Roads (cw = 7 m), Flat Terrain (BANG et al., 1995)

2.4.2 Typical Urban Road Capacity Measurement under Mixed Traffic Flow

Due to difficulties in finding a typical apparent knee concerning the relationship betweenspeed – flow, especially in a mixed traffic condition which results in difficulties in definingthe typical/ real capacity of a road. Therefore, there was the necessity to make different waysof measurement. Under mixed traffic conditions, some experiments in capacity have beendone, e.g. for 2/2 UD straight 7 m wide road with no side friction and shoulders > 1 m andthe capacity was estimated in different ways :

1. Direct observation of speed and flow rate average per 5–minutes. But only a fewobservations can be made due to lack of road sections with maximum flow that could beclearly identified as representing the capacity of the road section itself. The highest valueranges from 2,800 lvu/h to 3,000 lvu/h, see Figure 2-7.

2. Observation of flow rates during short periods of simultaneous bunching conditions inboth directions (headways < 5 sec). The capacity was found to be ranging from 2,800lvu/h to 3,100 lvu/h.

3. Theoretical estimation from speed – flow – density modeling that showing capacity ofaround 3,000 lvu/h occurring at a density of 81 lvu/km.


18

The capacity of an urban road section (IHCM, 1997) is determined as follows

CSSFSPKSW FCFCFCFCFCCC ⋅⋅⋅⋅⋅= 0

whereC = Capacity [pcu/h]C0 = Base capacity [pcu/h]FCW = Adjustment factor for carriageway width [-]FCKS = Adjustment factor for kerb and shoulders [-]FCSP = Adjustment factor for directional split or median [-]FCSF = Adjustment factor for side friction [-]FCCS = Adjustment factor for city size [-]

The base capacity of each type of road are presented at Table 2-5.

Type of Road Base Capacity, C0 [pcu/h] RemarksFour lanes – one way 1650 Per laneFour lanes – undivided 1500 Per laneTwo lanes – undivided 2900 Total two way

Table 2-5. Base Capacity, C0 for Urban Roads (IHCM, 1997)

2.4.3 Typical Conflicts at Unsignalized Intersections

Previously, characteristics of vehicles’ movement at unsignalized intersections under mixedtraffic flow with no existing rule of priority have been explained. All streams would have thesame opportunity to cross the intersection at the same time for motorized and unmotorizedvehicles. Due to a large scale of vehicles’ movement from the streams (6 streams), vehicles’conflict occurred very often. Based on such vehicles’ maneuvers at intersection, conflictbehavior could be defined as

■ Primary conflict: is the conflict that happens between crossing streams.■ Secondary conflict: is the conflict between right–turn streams and others streams, or between left–turn streams with other streams (e.g. pedestrian).

KATAMINE (2000) has classified the conflict at four–leg unsignalized intersections with 11types and he described them as

1. Left–turn, same direction conflicts2. Right–turn, same direction conflicts3. Slow–vehicle, same direction conflicts4. Lane change, same direction conflicts5. Opposing left–turn conflicts6. Right–turn, cross traffic, from right conflicts7. Left–turn, cross traffic, from right conflicts8. Through, cross traffic, from right conflicts9. Right–turn, cross traffic, from left conflicts

(2-4)


19

10. Left–turn, cross traffic, from left conflicts11. Through, cross traffic, from left conflicts

In such a mixed traffic condition with no rule of priority, it is very obvious that more conflicts(secondary conflicts) were found in this study, instead of primary conflicts. Therefore, thisstudy was deeply considered the secondary conflicts which happened very often and accidentshave not occurred during the observation with a very small percentage of vehicles’ stop. Sincestreams’ interactions have a very small impact on vehicles’ stops, this study was constructedto define some groups of conflict consisting of several streams which could give impact oneach other, result on speed reduction of stream within its group.

It have been explained briefly in the previous chapter on characteristics of unsignalizedintersections under mixed traffic flow. In this chapter, further improvements on capacityanalysis under mixed traffic were observed. Studies have been done in some countries whichhave almost the same traffic flow behavior and vehicles’ classes, e.g. lack of lane disciplineand presence of un–motorized vehicles. Studies have been done in Indonesia for several years(IHCM, 1997) and also in India (RAO, RENGARAJU, 1995; 1998) and China (BANG,HESHEN, 2000).

Investigations of vehicle–arrival at urban uncontrolled intersections have been done by RAO& RENGARAJU (1995) which result in multivariate models for estimation of vehicle arrivalswith various types of vehicles, i.e. motorized and un–motorized vehicles. The authors havedeveloped a model for a quick estimation of possible conflicts at urban uncontrolledintersections at low volume. The common features of traffic are lack of lane discipline,acceleration, deceleration and turning movements. The results show that conflict increaseswith increase in flow on cross–roads for any percentage of turning flow and high volume atthe main road.

Traffic conflicts must be considered to assess the quality of traffic flow at an uncontrolledintersection. In practice, this assessment is made to suggest appropriate traffic controlmeasures at an intersection. Furthermore, the exact consideration of the degrees of priority ofdifferent traffic streams at an intersection is still an unsolved problem. A study of theinteractions between crossing vehicles is important in evaluating traffic quality atintersections.

Studies have been undertaken to observe a comprehensive highway capacity study andguideline for Indonesia (BANG et al., 1997) and China (BANG et al., 2000). Data werecollected from several road links to obtain passenger car equivalents, free – flow speed, speed– flow – density relationship, cross section characteristics, road class, side friction and terrain


20

type. It was found that traffic flow, split between major and minor road traffic, level of ”sidefriction” and road width were the main variables influencing traffic performance. The totalactual capacity for all arms of the intersection is calculated as the product between a basecapacity under ideal conditions and a number of adjustment factors which gave impact oncapacity. Assessment to the capacity measurement under mixed traffic flow is proposed under

■ Geometric conditions; intersection entry widths, intersection types and major roadmedian

■ Environmental conditions; road environmental type (commercial, residential andrestricted access), side friction (consider : pedestrian, stopping vehicles, slow–moving vehicles and entrance/exit vehicles) and city size class (represent driverbehavior and vehicles populations)

■ Traffic conditions; left–turn, right–turn and split at minor road.

2.4.4 Typical Geometric Design Standard and Its Adjustment Factors

So far, the Indonesian manual has been used for planning and design purpose. Instead of that,there are also some standards to which must be referred while no details of explanation werefound in the manual, e.g. the standard design of unsignalized intersections. The standard ofgeometric design for intersections is based on the manual of ”Standar PerencanaanGeometrik untuk Jalan Perkotaan”, (Geometric Design Standard for Urban Roads)DIRECTORATE GENERAL OF BINA MARGA (1992) and ”Produk Standar untuk JalanPerkotaan” (Standard Product for Urban Roads), Directorate General of Bina Marga (1987).Early experiments took place at two way two–lane undivided intersections/UD (no–median)streets with a total effective width of 5 m – 6 m for both lanes and each way has anappropriate kerb/berm and side–walks with effective width of 0.5 m – 1.0 m in urban areas.Intersections are located in urban areas with high side friction value. All streams areconsidered to be equal in hierarchy of departure priority which means that no such signs ofstop and give–way.

Parameters which are taken into consideration are i.e., type of road, widths, availableshoulders, kerbs and medians with various widths. The existence of kerbs and berms at sideroads will give an opportunity to walk and ride along the edge of roads for pedestrians,cyclists, stopping terms for public transport and other activities. All of these activities arecalled ”side friction” which cause decreasing capacity and free–flow speed, but due to smallnumber of free–flow speed at urban roads, therefore, impact of road alignment can beneglected. The following Figure 2-8 performed geometric standards for road sections inIndonesia (IHCM, 1997).


21

Figure 2-8. Illustration of the Geometric Design Term (IHCM, 1997)

The manual explains that the geometric design has a great impact on the level of road safetyas can be estimated as :

■ Improvement of road widths can reduced the number of accidents between 2% –15% per meter road width.

■ Little traffic safety can be increased as improvement and wider road berm areincreased.

■ 10% – 30% of road accidents can be decreased if medians are built.

The number of accidents was estimated for each type of cross section and intersectioncorrespond to the number of vehicles entrance and it was presented below (Table 2-6).However, further studies will not analyze any relationship between traffic flow performance(flow, speed and number of conflicts) and safety (accident and fatalities), because in most ofthe cases, many accidents were not recorded well by the authorities (e.g. police).


22

Cross Section/Type ofIntersections

Estimation for Number ofAccidents Remarks

2/2 UD, CW = 5 m 2.332/2 UD, CW = 6 m 2.052/2 UD, CW = 7 m 1.802/2 UD, CW = 10 m 1.504/2 D 1.004/2 D 0.60Freeway UD 0.44Freeway D 0.33

Accidents per millionvehicles – kilometer

Unsignalized intersections 0.60Signalized intersections 0.43Roundabouts 0.30

Accidents per millionvehicles entrance

Table 2-6. Estimation for Number of Accidents in Indonesia Based on Type of Road Section and Type of Intersections (IHCM, 1997)

Type of intersections incorporated with the number of legs and lanes at minor and majorroads. Actual (current) measurement of geometric design could necessarily be made to findthe real width of approaches while it is important to adjust the number of lanes of approachand based on its type correspond to the analysis requirement from the manual. The basiccapacity can be adjusted based on intersection categories (number of lanes at each leg). Thefollowing Table 2-7 identifies the type of intersections.

Type ofIntersection Number of Legs Number of Lanes

at Minor RoadNumber of Lanes

at Major Road322 3 2 2324 3 2 4342 3 4 2344 3 4 4422 4 2 2424 4 2 4444 4 4 4

Table 2-7. Type of Intersections and Number of Lanes (IHCM, 1997)

The adjustment factor for the number of lanes based on the average width approach can beseen in Table 2-8 below. Investigation on each approach of intersections have been conductedto have the real widths in order to adjust the number of lanes of each leg of intersectionsrelated to the capacity analysis based on the Indonesian manual.


23

Average Widths Approach [m] Number of Lanes< 5.5 2

WBD ≥ 0.5 4< 5.5 2

WAC ≥ 5.5 4Table 2-8. Number of Lanes Based on Average Width Approach (IHCM, 1997)

Instead of that, it was also proved that presence of median (at the certain width) at two–lanetwo–way roads would have a very significant impact on the typical mixed traffic, e.g. improveflow, capacity and safety and most of the cases, medians were installed at major roads due tohigher level of flow. This will be discussed further. The following Figure 2-9 shows adifferent view of calculation for the average width of intersection entry. From the observation/field investigation it is found that the intersections have a large difference in widths of legsbetween one and another, therefore, in order to apply the model of capacity from theIndonesian manual, there must be an appropriate factor to be adjusted representing varyingwidth of approach. Investigation at fourteen (14) intersections have also found that most ofthe intersections did not meet any design standards (e.g. widths, radii). Inconsistency indesign will not be taken into account for further analysis based on developed models ofcapacity analysis.

Figure 2-9. Number of Lanes Based on Road Entry Width, W (IHCM, 1997)

Based on average intersection entry widths, WE and type of intersections, the adjustmentfactors, FW can be calculated by a linear model as shown in the following Table 2-9,

Type of Intersection Form422 FW = 0.70 + 0.0866 WE

424 or 444 FW = 0.61 + 0.0740 WE

322 FW = 0.73 + 0.0760 WE

324 or 344 FW = 0.62 + 0.0646 WE

342 FW = 0.67 + 0.0698 WETable 2-9. Adjustment Factor of Width Approach, FW (IHCM, 1997)


24

For a simplification, a following graph (Figure 2-10) was created by the manual (IHCM,1997) in order to calculate the value of adjustment factor, FW.

Figure 2-10. Entry Width Correction Factor, FW (IHCM, 1997)

One of the important facilities on roads and intersections is the road median which, under themixed traffic flow and rule of priority, does not exist at all. This facility would have a verysignificant impact on traffic flow performance. An impact of medians on major roads is thatthe vehicles have an opportunity to wait at the conflict area in order to pass through,especially a wider median and a constructed median (paved) would give a very clearinformation of two different stream directions (especially when lane discipline no longerexists). Therefore, if there is no median on major roads then the factor will be 1.00. Medianswith width < 3 m will have a factor of 1.05 and median with ≥ 3 m 1.20. Those wereperformed at Table 2-10 as follows

Remarks Type of Median Adjustment Factor ofMedian, FM

No median at major street none 1.00Median at major street,width < 3 m narrow 1.05

Median at major street,width ≥ 3 m wide 1.20

Table 2-10. Adjustment Factor for Median at Major Road, FM (IHCM, 1997)


25

2.4.5 Adjustment Factors for Traffic Flow Performances

Traffic flow of each stream is the most important parameter to study the capacity. Instead ofgeometric standard design of intersections, turning vehicles (from the major and minor roads)have contributed disturbance to others while they are making such maneuvers to pass throughthe intersections. Therefore, the manual was related to this problem and decided to use acorrection factor while measuring the capacity. Left–turning correction factor, FLT isestimated from Figure 2-11 below as well as right–turning correction factor, FRT which isused for 3–leg intersections. A suitable relationships between factors and portion of vehiclesis as followsfor left–turning correction factor,

%0161.084.0 LTFLT +=

and for right–turning correction factor,0.1=RTF (for 4 – legs)

%1022.909.1 3 RTFRT−⋅−= (for 3 – legs)

whereFLT = Adjustment factor for left–turning vehicles [-]FRT = Adjustment factor for right–turning vehicles [-]LT% = Percentage of vehicles turning left [%]RT% = Percentage of vehicles turning right [%]

For a simplification, the following graphs at Figure 2-11 and Figure 2-12 are presented.

Figure 2-11. Left–Turning Correction Factor, FLT (IHCM, 1997)

(2-5)

(2-6)


26

Figure 2-12. Right–turning Correction Factor FRT (IHCM, 1997)

The Highway Capacity Manual (HCM, 2000) states that the capacity of a two–lane road isnearly independent of the directional split of traffic, however, a study from CHANDRA &SINHA (2001) on two–lane roads in India shows that capacity reduces as the split movesaway from 50/50 and the capacity of such road under mixed traffic condition is a function ofthe split of traffic in two directions. BANG et al. (1995) developed speed–flow relationshipsand simulation model for two–lane roads in Indonesia. They found that under ideal conditionsfree speed is considerably lower in Indonesia than in developed countries.

On intersection under mixed traffic flow would be more complicated several streams consist.In such a case, split of traffic flow was defined as flow at minor road. This value depends onsuch number of portion of vehicles, ρMI (= 0.01 SP%) which pass through at minor road at thecertain time period. SP% is defined as percentage of portion split, ρMI (portion number ofvehicles at minor road). Empirical data measurements were graphically drawn in Figure 2-13.


27

Figure 2-13. Split Correction Factor, FSP (IHCM, 1997)

2.4.6 Adjustment Factors for Intersections’ Environment (”Side Friction”)

Large differences in behavior and level of development between places and cities in Indonesiagive a great impact on various drivers’ behaviors and vehicles’ population, e.g. age, power,performance and composition of vehicles (IHCM, 1997). Smaller cities show that drivershave driven non–modern vehicles (small power) and move slowly which results on lowerspeed and capacity at certain level flow rate, compared with other big cities. The manual hasconsidered that drivers’ behavior differed from modern cities and non–modern cities.Therefore, the factors are described to be higher when the number of people in such an area ishigher. It is assumed that more interactions occur within the traffic because there are morepeople living in the surrounding area of intersections.

City Size Inhabitant (million) Adjustment Factor, FCS

Very small < 0.1 0.82Small 0.1 – 0.5 0.88

Medium 0.5 – 1.0 0.94Large 1.0 – 3.0 1.00

Very large > 3.0 1.05Table 2-11. Adjustment Factor for City Size, FCS (IHCM, 1997)


28

Another typical characteristic on the roads and intersections in developing countries, e.g.Indonesia, is the people’s activities along the edge of the road or even at the lane of the roads.Activities along the road are very common in Indonesia which result in more conflict (”sidefriction”) and influence the flow. Side frictions could impact on capacity and road trafficperformance and activities that have been taken into consideration are

■ pedestrians;■ public transport and stopping vehicles;■ slow–moving vehicles (e.g. rickshaw, pushcart, etc.);■ entrance and exit vehicles from along the edge of roads

2.4.7 Total Capacity of Unsignalized Intersections under Mixed Traffic Flow

Studies for capacity under mixed traffic situations have been done, especially in developingcountries e.g. India and Indonesia where vehicles are categorized as fast–moving vehicles andslow–moving vehicles where the static and dynamic characteristics of these vehicles varywidely. RAMANAYYA (1988) has done a simulation model which was developed and testedwith a number of times for different traffic volumes and a different percentage mix ofvehicles. Traffic stream models (speed – flow, speed – density and flow – density) undermixed traffic conditions are essential to the formulation.

CHANDRA & SINHA (2001) and CHANDRA & KUMAR (2003) stated that the capacity ontwo–lane roads was influenced by directional split of traffic. The capacity reduces as splitmoves away from 50/50. The capacity of a two–lane road also increases with total width ofthe carriageway. IHCM (1997) determined the capacity of a road segment based on basiccapacity with various adjustment factors such as carriageway width, kerb and shoulders,median and directional split, side friction and city size. In such a case, it is very difficult tomeasure the capacity due to poor lane discipline which exists including a tendency to ”cutcorners” while drivers making right–turn which results in a blockage of other trafficmovements. Studies of the driver’s behavior in China showed that only 40% of the vehiclesthat had a choice between ”gapping” and ”pushing” actually waited for a gap in the majorroad flow, i.e., gap acceptance models could not be used to predict intersection performancefor unsignalized intersections.

The fundamental concept to measure the capacity of unsignalized intersections by anempirical approach (IHCM, 1997) is that the flow and capacity was calculated as the totalflow (entering flow) and the total capacity of the intersection, instead of capacities of each legof intersection. A scheme of traffic flow streams is shown in Figure 2-14 and the total flow iscalculated by the following formula in Equation 2-7 to Equation 2-9. This total flow is thetotal number of passing vehicles per hour pass through the intersection (streams; QA, QB, QC

and QD).


29

Figure 2-14. Scheme of Flow Streams at Unsignalized Intersection (IHCM, 1997)

The total entering flow is calculated as

DCBAQEF +++=

where

QEF = Total entering flow [veh/h]A or QA = UMMCHVLV QQQQ +++ [veh/h]p = PCUs – factor calculated from pcu values flow composition [-]

100)pcuUM%pcuMC%pcuHV%pcu(LV%p UMMCHVLV ⋅+⋅+⋅+⋅

=

therefore, the total flow, QTOT can be calculated as

pQQ EFTOT ⋅=where

QTOT = Total entering flow [pcu/h]ρLT = Portion of left–turn flow [-]ρRT = Portion of right–turn flow [-]ρMI = Portion of flow at minor road [-]ALT, BLT, CLT, DLT = Flow of left–turn from leg A, leg B, leg C and leg D [veh/h]ART, BRT, CRT, DRT = Flow of right–turn from leg A, leg B,leg C and leg D [veh/h]QEF = Total entering flow [veh/h]QLV, QHV, QMC, QUM = Flow of light–vehicle (LV), heavy–vehicle (HV),

Motorcycle (MC) and Unmotorized (UM) [veh/h]

(2-7)

(2-8)


30

As traffic stream in developing countries is heterogeneous, consisting of different types ofvehicles which were defined as different in static and dynamic characteristics, derivingpassenger car units (PCUs) are required, and the typical analysis of the total flow at a roadsegment is

UMMCHVLVV QQQQQ +++=

whereQV = Total flow at road section [veh/h]QLV = Traffic flow of light–vehicle [veh/h]QHV = Traffic flow of heavy–vehicle [veh/h]QMC = Traffic flow of motorcycle [veh/h]QUM = Traffic flow of unmotorized–vehicle [veh/h]

Base capacity, C0 is defined as capacity under ideal traffic conditions with no impact of suchside frictions, left–turn traffic, right–turn traffic and unmotorized. The value of this capacitysolely depends on the types of intersections (Table 2-12).

Type of Intersections Base Capacity, C0 [pcu/h]322 2700342 2900

324 or 344 3200422 2900

424 or 444 3400Table 2-12. Base Capacity of Unsignalized Intersection, C0 (IHCM, 1997)

Capacity at unsignalized intersections is defined as a result of basic capacity within idealtraffic conditions related to various adjustment factors and corrections which consider theimpact of road environment, geometric design of road and traffic conditions. As it is definedin the INDONESIAN HIGHWAY CAPACITY MANUAL (1997), capacity can be calculatedas

MIRTLTRSUCSMW FFFFFFFCC ⋅⋅⋅⋅⋅⋅⋅= 0

where

C = Capacity [pcu/h]C0 = Base capacity [pcu/h]FW = Adjustment factor for width of approach [-]FM = Adjustment factor for median at major road [-]FCS = Adjustment factor for city size [-]FRSU = Adjustment factor for type of environment, side friction and unmotorized [-]FLT = Adjustment factor for left–turn [-]FRT = Adjustment factor for right–turn [-]FMI = Adjustment factor for ratio of traffic at minor road [-]

(2-9)

(2-10)


31

Since there are no clear definitions of major and minor roads and the model is not based on”gap behavior”, the current model can only measure the capacity of intersections (all legs) asthe total capacity of intersections while the capacity of each stream at the intersection couldnot be measured.

2.5 Conclusions

The Indonesian traffic flow situation has a typical mixed traffic flow which is consisting ofvarious types of vehicles travelling at the same lane of a road. More than 13 classes ofvehicles which can be defined as fast–moving vehicles and slow–moving vehicles, whichhave a large difference in static and dynamic characteristics exist. Instead of that, lack of lanediscipline could also promote a great impact on capacity and performance of traffic on roadsand intersections. Such drivers’ behaviors, e.g. no gap acceptance behavior and no lanediscipline, would indicate that models from developed countries would not be suitable forIndonesia.

Therefore, a method of capacity analysis has been created in the Indonesian manual and it hasbeen used for planning and design purposes. So far, the manual could be implemented forunsignalized intersections in condition that ”the rule of priority” and ”gap acceptance” do notexist. The method used an empirical approach by the analysis of a large amount of data fromintersections at several cities in Indonesia which took three years of observations. From themanual, the real capacity of unsignalized intersections was calculated by using a basiccapacity value adopted from an ideal traffic condition and several (adjustment) factors as animpact from geometric design, traffic composition, and environment of intersections.

However, the method has not given any information on how the streams interacted with eachother. Interactions between streams corresponding to the speed and flow were not clearlydescribed. This method has calculated the capacity as the total capacity of intersections basedon all traffic streams. That means, it could not be determined how large the traffic flow fromeach stream has contributed to the capacity and it is difficult to find an ideal condition ofunsignalized intersections in order to adopt the basic capacity because each city in Indonesiahas its own traffic and environmental characteristics. Therefore, all adjustment factors relatedto the geometric design, traffic flow, and intersection environment have to be considered andchecked further in order to receive results suitable for the intersections observed.

3 Capacity Computations Based on Rule of Priority Method

32


3.1 Introduction

Capacity at unsignalized intersections is measured with various approaches known asdeterministic and probabilistic approaches. Gap acceptance procedure (GAP) is mainly usedin the United States and several European countries. This method is based on critical gapacceptance and follow–up times of vehicles from the minor road. The second method is theempirical regression approach, its application is mainly based on research investigation fromBritish research results (KIMBER, COOMBE, 1980). This method is developed by a largenumber of measured field data in British streets. A new approach is called conflict technique(WU, 1999) which is based on a pragmatically simplified concept where interactions andimpact between flows at intersections is brought through mathematically formulated. TheINDONESIAN HIGHWAY CAPACITY MANUAL (IHCM, 1997) is an example of usingthe empirical approach, however, due to current behavior, e.g. no gap acceptance behavior,unmotorized attendance with 13 classes of vehicles and large different speed, no exclusivelanes, no lane discipline and a large number of conflict might be expected. Therefore,investigation on total basic capacity, C0 and total actual capacity, C of intersection shouldnecessaries be done. The Indonesian Highway Capacity Manual is the current approach tomeasure the capacity and traffic based on a research study of the Swedish Road and TrafficResearch Institute (SWEROAD) which was conducted in Indonesia from December 1990 toFebruary 1997.

Another common type of intersection has the rule ”priority–to–the–right” over another. Thistype of intersection does not have a site–specific priority control and it does exist in GreatBritain and some developing countries, namely in relatively dense areas and residential areasor when road–works are carried out. Many countries have adopted the basic priority rules toapply specifically to this kind of intersection. Although they are very different in someaspects, they evolved from a basic principle of the type; a driver should give way to a vehicleapproaching the intersection from his right (also called ”nearside” priority rule in countrieswhere vehicles use the right side of the road and ”offside” priority rule in countries using theleft side). It has been noted, however, that frequently drivers’ understanding of the rule is poorand, perhaps because of that, in some cases the intersections seem not to be working correctlyfrom the priority rule point of view.

All–Way Stop–Controlled intersections require drivers on all approaches to stop beforeproceeding into the conflict area at an intersection. All streams are equal (since there are stop–signs in all approaches, it assumes to be equal) in the hierarchy of the priority of departure,therefore, the vehicles of different streams should enter the intersection one streams’ vehicleafter another streams’ vehicle. All–Way Stop–Controlled (AWSC) intersections are verypopular in the United States and many countries in North America and First–In–First–Out


33

(FIFO) intersections are used in most of the developing countries. Studies concerning AWSCand FIFO are very limited and some studies have been conducted with regard to the generalanalytical procedures for AWSC. However, it is not possible for it to handle more variedconditions in the real world. Because the departure priority is similar for both types ofintersection, AWSC and FIFO intersections can be treated in the same manner.

Capacities under mixed traffic/heterogeneous flow are different from those of homogeneoustraffic flow. Since there are no traffic signs or traffic signs exist (priority junctions) but due tolack of traffic discipline, the drivers tend not to follow the rules of the priority. With thosecurrent points of view – the available procedures to handle a systematic and realistic analysisof the traffic process is very important. Therefore, since there are neither available signs atintersections nor common rules (give way and traffic behavior model based on gapacceptance) the traffic mechanism at First–In–First–Out intersections might not be followed.

3.2 Nearside/Offside Priority Intersection

One of the concerns of investigators studying uncontrolled intersections has always been thedevelopment of a hierarchy of priority regulations together with a comprehensive set of ruleswhich ought to enable the determination of the most adequate solution for each intersectiondepending on its geometry and traffic characteristics. Many countries in the world have abasic ”near/offside priority rule” which applies to all intersections where no priority signs ormarkings are shown. It has been noted, however, that frequently drivers’ understanding of therule is poor and, perhaps because of that, in some cases the intersections seem not to beworking correctly from the priority rule’s point of view.

Three areas of research were identified as important : study of drivers’ approaching patterns;description of drivers’ interactions and decisions when trying to enter an intersection workingunder the ”priority–to–the–right” rule and evaluation of the performance of the priority ruleboth in relation to its ability to regulate light traffic conditions and in relation to safety. It wasthought that the detailed study of drivers’ behavior during their approach to intersectionscould be very helpful as a complement to the studies directed to the evaluation of the priorityrule performance: the analysis of the regulation ability by the priority rule is very muchconcentrated on the drivers’ action at the entrance to and inside the intersection, with anyevents happening before being ignored, the safety evaluation is based on the integratedquantification of a number of subjective parameters which, although enabling theidentification and classification of dangerous situations, does not allow any attempt to relatethe occurrence of those events to the different driving styles during the approach to theintersections and in the application of the priority regulations.

A typical three–leg unsignalized intersection under ”near/offside priority rule” with six typesof movements (streams) is performed in Figure 3-1. Three streams (EN, NW and WE) have


34

priority in any circumstance and can be considered to be first–level priority movements. Eachof the other three movements has priority over one of the remaining two (NE over EW, WNover NE, EW over WN) and has to give way to the other as well as to one conflicting first –level priority movement. These three movements can be considered to be second–levelpriority movements. However, it is important to record that the EW movement is of the typewhere incorrect behavior based on ”natural expectancies” was identified in previous studies.

Figure 3-1. Hierarchy of the Possible Movements in A Three–Leg Unsignalized Intersections (SECO, 1991)

SECO (1991) conducted an observation at some unsignalized intersections under a”near/offside priority” rule in Portugal. Three typical situations were selected for analysis(stream EW, stream NE and stream WN) corresponding to some of the most interesting gapacceptance problems and each involving decisions by different second–level prioritymovements. The methodological approach applied on the analysis of these situations usingbasic gap acceptance concepts to describe the decision process made by non–priority driverswhen, in order to enter an intersection, they have to select an acceptable gap (measured intime intervals) in the priority streams on traffic.

He found that lower percentages of accepted lags did occur when the previous departure wasof type EW or NE and happened less than 4 seconds before the WN vehicle arrival. Thehigher percentages corresponded almost always to the situations when the previous movementalso was of type WN and happened less than 4 seconds before the WN conflicting vehiclearrived. It can be concluded that in case of conflicting streams EW, NE and WN where a


35

stream is superior over another lags in a range of 3.0 seconds – 6.0 seconds would beproduced. However, such analysis requires a condition of traffic that follows a well–known”priority–to–the–right” rule which does not exist in most of the developing countries.

KOCKELKE (1991) has investigated some intersections in Germany regulated by the rule”priority–to–the right” (Rechts–vor–Links Prinzip) (Figure 3-2). This type of intersectionwould have the same behavior compared with ”near/offside priority” as has been previouslyexplained, but he has made investigations in advance corresponding to the typical speedbehavior at the intersection. KOCKELKE & STEINBRECHER (1983) conducted anexperiment at intersections under the ”priority–to–the–right” rule (rechts–vor–links) whichrelates to the quality measurement. The quality of an intersection was performed by ”lost–time” [additional (lost) – time] while vehicles travel through the intersection. Within the”slowing– phase” (decreasing speed) of a vehicle, the experiment has a distance ∆s ≈ 90 mand ∆s ≈ 40 m and would take additional (lost) – time 1.8 seconds and 0.8 seconds. Resultsshowed that the average decreasing speed is about ∆v ≈ 18 km/h (see Figure 3-3). The studieshave given an indication of existence of speed reductions of vehicles while travelling throughconflict areas of intersections and they decelerated the speed instead of totally stopping. Ascan be seen in Figure 3-3, vehicles travelling from major road A straight to road D wouldhave an average speed at the conflict area of about 27 km/h.

Figure 3-2. Typical Speed Pattern at ”priority – to – the – right” Intersections (KOCKELKE, 1991)


36

Figure 3-3. Typical Speed – Profile with ”Priority – to – the – Right” Rule (KOCKELKE, STEINBRECHER, 1983)

3.3 Capacity of ”Priority – to – the – Right” Intersection

An unsignalized intersection with the rule of ”priority–to–the–right” would be clear definedwith low traffic flows at intersections whose traffic streams could pass through theintersection one after another. However, this type of intersection might have a problem whenthe traffic flow is high and all streams are approaching the intersection at the same time,therefore, the streams would block each other, see Figure 3-4.

Figure 3-4. Scheme of Traffic Flow Blocking at ”Priority – to – the – Right” Intersection (Wu, 2003)

q1, C1, x1

q1, C1, x1

q1, C1, x1

q1, C1, x1


37

WU (2003) has developed a mathematical model for 4–streams/4–leg intersections in order tocalculate the capacity of each stream which is based on the theory of gap acceptance andfollow–up time. The capacity of the stream i is calculated as :

( ) iii CxC ,011 ⋅−= +

withCi = Capacity of stream i [veh/h]

⎟⎟⎠

⎞⎜⎜⎝

⎛∆−−⋅−

++

⋅⎟⎠⎞

⎜⎝⎛ ⋅∆−⋅= 236001

,,0

,,

1

360013600

ifig

i tt

q

i

ifi eq

tC [veh/h]

= Basic capacity of stream i [veh/h]tf,i = Follow–up time of stream i [sec]tg,i = Gap acceptance of stream i [sec]∆i = Minimum time between two consecutive vehicles i [sec]qi = Traffic flow of stream i [veh/h]

xi+1 = 1

1

+

+

i

i

Cq = Degree of saturation of stream i + 1 [-]

In Equation 3-1 we can see that the capacity of stream i, Ci is a function from Ci+1 and Ci+1 isa function from Ci+2 and so on. If we consider that i = 1 to N, therefore, an example forN = 3 we find

( )

1,0

2,0

3,04

4

3

2

1,021

11

1

1

C

CC

Cqq

q

CxC

⋅

⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

⋅

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

⋅⎟⎟⎠

⎞⎜⎜⎝

⎛−

−

−=

⋅−=

From the equation, it is requested to have an appropriate gap and follow–up time of eachstream at the intersection which is almost not possible to measure it at the intersection undermixed traffic flow, therefore, this approach might not be applied to such condition,appropriately.

3.4 Capacity Measured by Saturation Flow of Streams

The theory of gap acceptance is a commonly used to predict the capacity, in which thevehicles of a non–priority stream are assumed to move into naturally occurring gaps in theappropriate priority stream. However, although gap–acceptance theory describes an importantaspect of vehicle interactions, there are several difficulties when it is used for practical

(3-1)


38

estimations of capacity (KIMBER, COOMBE, 1980). The time gaps are not easy to measureand the capacity calculations are sensitive to the values used. The rules governing theinteractions of more than two streams are not very clear.

Major/minor priority is the most common form of intersection control. Mostly, it isappropriately used at intersections where it is desirable to give priority to one route, usuallythat is carrying the greater traffic volume. The vehicle – vehicle interactions that determinethe capacities are complex and in most cases it is straightforward to determine therelationships between the stream capacities and the factors affecting them empirically – todeal directly with the traffic flows themselves. Linear function was developed between streamcapacity and controlling major road flows,

∑−=i

iiqαCC 0

where

C = Stream capacity [pcu/h] αi = Degree of traffic interaction between stream i and controlled stream [-]C0 = Saturation flow – the value the capacity would take if all major road

flows were zero [pcu/h]qi = Major road flows [pcu/h]

The essential structure for determining each of the coefficients as functions of the intersectiongeometry was analyzed in two parts. The first is to determine which major road flows affect agiven controlled stream and second is to develop the geometric relationships specifying thecoefficients of those. The flows and notation of major/minor priority is shown in Figure 3-5.

Figure 3-5 .Flows and Notation of Major/Minor Priority Intersection (KIMBER et al., 1980)

(3-2)


39

Effects of traffic composition on the stream capacities from two sources – variations ofcomposition in the controlling major road flows and in the minor road streams – and thedecision which major road flows are relevant to the determination of a given minor roadstream were investigated by using the equation which includes all four major road flows withdifferent portions of types of vehicles (light and heavy vehicles) with a form of equation

(HV)qα(LV)qα(HV)qα(LV)qα

(HV)qα(LV)qα(HV)qα(LV)qαCpqqC

BCBCACAC

BABACACAHVLVmi

−−−−

−−−−

′−−′−

−′−−′−−=+=

4433

22110

where

Cmi = Minor road stream capacity [pcu/h]qLV and qHV = Flows of light and heavy vehicles [pcu/h]qA-C(LV), qA-C (HV) = Major road flows of light and heavy vehicles [pcu/h]α1, α2, α1', α2' , ... = Effects of the major road flows on Cmi [-]p = (α1'/α1), (α2'/α2), (α3'/α3), (α4'/α4)

= Passenger car units [-]

Concerning the major/minor priority rule not each stream would have the same opportunitywhile travelling cross the intersection. Therefore, further investigation on vehicles’interactions between streams should be taken into account. The effects of the major roadstreams were assessed from the values of the regression coefficients α1, α2, α1', α2', α1'', α2'',....In general, all major road flows have an effect on the minor road right–turning and left–turning stream capacity, interactions between streams can be formed asleft–turning capacity,

BACAC,BCB qαqαCC −−−− −−= 210

right–turning capacity,

BCACBACAA,BAB qαqαqαqαCC −−−−−−′−′−′−′−= 43210

Corresponding to other streams (minor and major), interactions between the right–turningmajor road flow controls qB-A and is controlled by qA-C and qA-B (give away) and followthe form of

BACAB,CBC qαqαCC −−−−″−″−= 210

A straight–through major road stream, CC-A at some of geometric layouts could be blockedwhen a queue exists by right–turning vehicles, qC-B. If arrivals and departures are random for

(3-3)

(3-4)

(3-5)

(3-6)


40

right–turns vehicles, the probability of a queue can be simply presented by qC-B / CC-B,therefore, the straight–through capacity, CC-A is given by

⎟⎟⎠

⎞⎜⎜⎝

⎛−=

−

−−−

BC

BCA,CAC C

qCC 10

where

CB-C = Capacity of left–turning flow from minor road [pcu/h]CB-A = Capacity of right–turning flow from minor road [pcu/h]CC-B = Capacity of right–turning flow from major road [pcu/h]CC-A = Capacity of straight–through flow [pcu/h]C0,B-C = Saturation flow of left–turning flow from minor road [pcu/h]C0,B-A = Saturation flow of right–turning flow from minor road [pcu/h]C0,C-B = Saturation flow of right–turning flow from major road [pcu/h]C0,C-A = Saturation flow straight–through flow [pcu/h]α1, α2, α1', α2',α1'', α2'',.... = Effects of the major road flows [-]qA-C = Major road flow of stream A – C [pcu/h]qA-B = Major road flow of stream A – B [pcu/h]qC-A = Major road flow of stream C – A [pcu/h]qC-B = Major road flow of stream C – B [pcu/h]

3.5 Capacity of All–Way Stop–Controlled and First–In–First–OutIntersections

A new theoretical approach for the determination of capacities at All–Way Stop–Controlled(AWSC) and First–In–First–Out (FIFO) intersections based on the Addition–Conflict–Flowmethod is developed from the graph theory (WU, 1999). The procedure is considered in sucha way that the First–In–First–Out discipline is applied. FIFO intersections are broadly used inthe developing countries (e.g. China, India and Indonesia). The AWSC intersections areconsidered in such a way that the First–In–First–Out discipline applies, because this type ofintersection does not posses an absolute priority in driving. In the HCM (1994 & 2000), anempirical approach is applied in order to calculate the capacity by regression of field data,however the result can only be determined by iteration and the method could not give anyinformation on interactions between streams. Information and consideration on streaminteractions are very important to look further, especially if the traffic consists of varioustypes of vehicles with different static/dynamic performances and drivers’ behavior.

The following procedure (conflict technique) would have the ability to count the totalintersection capacity considering (a) the number of lanes at the approaches, (b) thedistribution of traffic flow rates, (c) the number of pedestrians at the approaches, (d) the flaredarea at the approaches, and (e) the interaction between the different streams. By using this

(3-7)


41

method, capacity was calculated corresponding to the interactions between streams whichthought to be more realistic for such rule of equal hierarchy of departure streams.

3.5.1 Departure Mechanism at AWSC/FIFO Intersections

Since all streams at AWSC/FIFO intersections are considered to be equal in the hierarchy ofthe priority of departure, the vehicles of different streams must enter the intersectionalternatively, see Figure 3-6.

Figure 3-6. Three Streams in A Departure Sequence (WU, 1999)

The vehicles in different streams have to pass the same conflict area alternatively one afteranother. Every vehicle of the stream i occupies the conflict area by exact tB,i seconds. In thecase of only two streams this corresponds to the rule of zipping. That means all streams musthave the same capacity in a departure sequence if all traffic flows Qi exceed their capacities Ci

(total overload) (WU, 1999). That is, the capacities of all streams in one departure sequencehave – under overload condition – the same value of

∑==

iBi t

CC,

3600 for Qi ≥ C

where

Ci = Capacity of stream i [veh/h]Qi = Flow of stream i [veh/h]tB,i = Headways departure of stream i [sec]

The capacity C is equal to the number of the seconds within an hour divided by the sum of theaverage departure headways of all involved streams, tB,i.

3.5.2 Capacity of A Stream in Several Departure Sequences.

A further investigation found that intersections with more than 12 streams flow would bemore complex, therefore, the model should be advanced for several departure vehicles. The

(3-8)

12

33

1

2


42

model has performed that the capacity of a stream in several departure sequences is the leastcapacity that this stream obtains in all of the departure sequences,

( )MINBsequenceAsequence CCC ,...,=

Figure 3-7. A Stream Involved in Several Departure Sequences (WU, 1999)

And at intersections of two two–lane streets, there is only one traffic lane in each of theapproaches. It is assumed that each turning movement has its own traffic lane at theintersection. The share–lane situation is computed later using the well known share–laneformula from HARDERS (1968).

The streams are incompatible with each other and they can only enter the intersectionalternatively. A stream at AWSC/FIFO intersections is always involved in several departuresequences. The smallest capacity, which a stream can achieve from these departure sequences,is the decisive capacity. It is hereby assumed that vehicles of two streams, which arecompatible with each other, can enter the intersection simultaneously.

The capacity of a stream i in a departure sequence with n streams reads

⎪⎪⎩

⎪⎪⎨

⎧ ∑

∑

=≠=

=

−

iB

n

ijjjB

n

jjB

t

tQ

t

iC )(

).(3600

)(

3600

,1

1

max

where

Ci = Capacity of subject stream i [veh/h]tB = Average departure headways/occupation time [sec]i = Subject streams i [-]j = Conflict streams j involved [-]

(3-9)

(3-10)

3

1

2

A

B

3

1

2

A

B


43

3.6 Conclusions

Several methods of capacity analysis at unsignalized intersections have been done indeveloped countries. Of course, they have considered the capacity based on a fundamentalpattern, rule of priority that vehicles are allowed to cross the intersection one after another.Common rules of priority intersection are known as ”nearside/ offside priority”, ”priority–to–the–right” or ”rechts–vor–links”, ”All–Way Stop–Controlled” or ”First–In–First–Out”. Ingeneral, the methods have considered each stream as the potential parameters to contribute thecapacity of intersections, especially for non–priority streams which might be saturated at acertain duration of time.

At ”priority–to–the–right” rule, potential capacity might occur at non–priority streams ofB – C, B – A and C – B which would face delay while they have to wait at the stop line asthe priority streams travel cross the intersection. Therefore, the capacity of the streams werecalculated based on the saturation flow of non–priority streams (all major road flows are zero)and the degree of interaction between streams (priority and non–priority). By using thismethod, effects of various types of vehicles and geometric designs were deeply concerned.

Another type of intersection is based on All–Way Stop–Controlled (AWSC) and First–In–First–Out (FIFO) rule. FIFO intersections are broadly used in developing countries which wasmore realistic because no traffic streams at intersections possess the absolute priority ofdriving and vehicles from different streams enter the intersection alternatively one afteranother. The new method to calculate the capacity has already been found (WU, 1999) basedon Addition–Conflict–Flow (ACF) where the capacity was calculated by consideringinteractions between conflict streams and every vehicle of the stream occupies the conflictarea by the exact time which is considered as headways departure of the stream. Thecapacities were then measured as a function departure headways of each stream and the totalcapacity of the intersection is the least capacity of capacities of several departure sequences.

However, capacity analysis based on such a rule of priority sounds to have a ”departure andarrival pattern” because vehicles enter one after another, therefore, those methods are alsofound very difficult to be applied properly under such a mixed traffic flow. Methods ofcapacity analysis based on such rules (priority and ”departure and arrival pattern”) shouldpossess a kind of a drivers’ discipline. The drivers have to really understand the rule andshould obey it, otherwise, the method could not be implemented well. Lack of drivingdiscipline and poor understanding of such a rule would cause the intersections to workcorrectly from the priority rule’s point of view. Typical traffic behavior in developingcountries is the lack of discipline, vehicles’ stop/wait less than 2 seconds and every vehiclefrom streams has the tendency to ”cut corners” while making right turns, therefore, again, themethods used in priority rule could not be implemented in developing countries appropriately.

4 Field Measurement and Data Performance

44


4.1 Introduction

In order to find representative data base which gives an overview of the real situation ofmixed traffic flow and unsignalized intersections where rules of priority do not exist,investigations have been undertaken in two cities, Pontianak (West Kalimantan) andYogyakarta (West Java) in Indonesia. Both cities would have typical mixed traffic flow whichcontents various types of vehicles, motorized and unmotorized vehicles. The cities are locatedon different islands, but people and traffic flow represented the same behavior. However,most of the intersections were observed in the city of Pontianak. Different to many developedcountries traffic travels on the left.

Like other cities in Indonesia, Pontianak is a developing city but it is located on the largestisland. Most of the people work in the office and business, because the area is not goodenough for people to cultivate their land and there are only two seasons, dry and rainyseasons. People tend to travel with their own vehicles (private) due to a lack of suitable publictransport, and also due to a lack of suitable road infrastructures. Most people chose economic(low cost) and efficient vehicle, e.g. motorcycles, because they feel that this type of vehiclesare cheaper than others and very suitable to operate them at the narrow streets which arepredominant in the city. Therefore, motorcycle ownership has increased each year and thepercentage of motorcycles is higher than of other types of vehicles.

4.2 Location of Field Measurements

Investigations at fourteen intersections which all consist of various width of lanes have beenundertaken. However, not all the intersections have been measured and analyzed. Tenunsignalized three–leg intersections have been investigated with various average widths ascan be seen in Table 4-1. These intersections have been investigated and counted further.They are located in the city of Pontianak, West Kalimantan and secondary data was collectedfrom Yogyakarta, Java. Those places have been chosen because they have a highly variedmode of transport, e.g. motorized and unmotorized which is very important for furtheranalysis.

The city of Pontianak is one of the developing cities in Indonesia. Its distance is not more than1000 miles away from Jakarta with a number of residents of about 2 millions. Data from1998 show that there are 16,825 km of primary arterial roads, 35,075 km of secondary arterialroads, 7,250 km of primary collector roads and 39,149 km of secondary local roads. Basedon investigation, each road has large different width even at the main road (e.g. approach Adiffers from approach C at the intersection). Typical geometric performance/lay–out can beseen in Figure 4-19. The location of all intersections is shown in Figure 4-1. In addition to 10


45

three–leg intersections, four others were also investigated but are not included in the analysisdue to the small number of vehicles.

Figure 4-1. Location of 14 Three–Leg Intersections

Three–leg intersections have been investigated which have typical lay–outs that approachA and C as the major road and approach B as the minor road. Referring to Figure 4-19 eachapproach would have large different widths. However, in further analysis, it does not matterwhere the major and the minor road are placed, because advanced methods would only use anumber of vehicles (flows) without considering the geometric design. Of course, in theIndonesian Manual it is indicated that each type of road would be given an adjustment factor.

3

9 6

81

WEST KALIMANTANINDONESIA

4105

2 7

Investigated and measured

Investigated

1


46

Therefore, this criterion is an important parameter if we use the manual. Details of geometricdesign/effective width of each approach can be seen in Table 4-2. Widths of each approachwere measured based on the original pictures designed by the authority and they were alsomeasured manually on the field.

Intersection Roads

1 Hasanuddin – Komodor Yos Sudarso – Pak Kasih2 K. H. Wahid Hasyim – Hasanuddin3 Komodor Yos Sudarso – Tebu4 Tanjung Raya – Panglima Aim5 Sultan Abdurrahman – Putri Candramidi6 Alianyang – K. H. Wahid Hasyim – K. H. Ahmad Dahlan7 Hasanuddin – Merdeka8 R. E. Martadinata – Tabrani Ahmad9 Dr. Wahidin – Husein Hamzah

10 W. R. Supratman – R. SupraptoTable 4-1. Investigated Three–Leg Intersections

Effective Width [m]Intersection

Approach A Approach B Approach C1 16.4 10.7 9.02 10.6 19.5 10.63 9.6 6.5 8.04 7.4 5.0 6.25 10.0 6.5 10.06 11.8 8.8 12.47 9.2 9.0 9.28 6.7 5.8 5.09 7.2 7.5 7.210 7.3 9.0 9.4

Table 4-2. Details of Geometric Design of Each Intersection

It has been explained in section 3 that effective lanes width of roads are determined based onthe manual. They have a range between 2–lanes and 4–lanes as can be seen in Figure 2-9. Thedata has been presented in Table 4-3. The number of lanes of each approach have variousdifferences one and another, sometimes a minor road would have wider lanes than a majorroad. The type of intersections is determined based on the number of lanes as indicated in themanual, see Table 2-7 and Table 4-4. Each of the investigated intersections has been definedas can be seen in Table 4-2 and Table 4-3.


47

Approach A Approach B Approach CIntersection

Width [m] No. Lane Width [m] No. Lane Width [m] No. Lane1 16.4 4 10.7 2 9.0 42 10.6 2 19.5 4 10.6 23 9.6 2 6.5 2 8.0 24 7.4 2 5.0 2 6.2 25 10.0 2 6.5 2 10.0 26 11.8 4 8.8 2 12.4 47 9.2 2 9.0 2 9.2 28 6.7 2 5.8 2 5.0 29 7.2 2 7.5 2 7.2 2

10 7.3 2 9.0 2 9.4 2Table 4-3. Effective Lane Width of Each Approach of Intersection

Intersection Type of Intersection Number of Laneat Major Road

Number of Laneat Minor Road

1 324 4 22 342 2 43 322 2 24 322 2 25 322 2 26 324 4 27 322 2 28 322 2 29 322 2 2

10 322 2 2Table 4-4. Type of Intersection Based on Number of Lanes

4.3 Equipment for Monitoring

In order to have detailed information of the traffic flow, we have used two cameras and wehave chosen the intersections whose geometric design was classified as medium category.Ordinary two camcorders with specification of DCR–TRV 270E PAL and two hours durationcassette model Hi8 are used to monitor each intersection. Additionally, a 3.5 meter highsupport is positioned at the edge of road nearly at the intersection corner. A total height of 3.5meters is enough to cover the essential points and have a very good view of the intersection,see Figure 4-2. Those cameras are placed at the edge of the roads or intersections with a goodview for monitoring the traffic movement (Figure 4-3).


48

Figure 4-2. Equipment for Traffic Monitoring Figure 4-3. Equipment Position at the (Camcorder) Edge of the Road

4.4 Vehicle Classifications and Compositions

The Indonesian Highway Administration distinguishes between 13 classes of vehicle for itsroutine classified counts. The study from BANG et al. (1995) has been carried out with thefollowing seven vehicle classes and the criteria for vehicles were distinguished as in Table 4-5. In this study, the type of vehicles is given in five main classes (LT, MHV, LV, MC, UM)while each main class could consist of several other vehicles. Furthermore, those main classesare considered based on the speed performance corresponding to the static (width and lengths)and dynamic (speed) characteristics. Vehicle types were grouped as LT (Light Trucks), MHV(Medium Heavy Vehicles), LV (Light Vehicles), MC (Motorcycle) and UM (Un–Motorized) see Table 4-6. This grouping is mainly based on the dynamic characteristics ofeach type of vehicle, e.g. speed, which one performed slightly different with another.

3.5 m


49

Types of Vehicle ClassesPassenger carsJeepsMinibusesPickupsMicrotrucks

LV (Light Vehicle)

Truck two-axle with double wheels on therear axleBuses shorter than 8 m

MHV (Medium Heavy Vehicle)

Truck three-axle LT (Large Truck)Truck plus trailerArticulated vehicle TC (Truck Combination)

Buses longer than 8 m LB (Large Bus)Motorcycles MC (Motorcycle)TricyclesBicycles UM (Un–Motorized)

Table 4-5. Vehicle Classification Based on the Indonesian Highway Administration

Type of Vehicle ClassesTruck 3 axle LTTruck 2 axleBus MHV

Car LVMotorcycle MCBicycleBecak (Rickshaw)TricyclesPushcart (2-wheels)

UM

Table 4-6. Main Vehicle Classification

Figure 4-4. Bicycle Figure 4-5. Tricycles/Pedicab/”Becak”


50

Figure 4-6. Tricycles Figure 4-7. Pushcart

The type of unmotorized vehicles from the field investigation which consists of five differentvehicles and they would have large different in dynamic characteristic (speed) and alsodifferent purposes, see Figure 4-4 to Figure 4-7. Therefore, it is necessary to make anotherclassification for them for further analysis. Classification for unmotorized vehicles mightchange and is more complicated than what has been made previously, e.g. UM isdifferentiated by UM1 (bicycle), UM2 (rickshaw/pedicab), UM3 (tricycles), UM4 (pushcart). Itwas also justified that category Medium Heavy Vehicle (MHV) was divided into MHV1 (truck2–axle) and MHV2 (Minibus). All categories are defined as shown in Table 4-7.

Vehicles CategoryTruck 3 axle LTTruck 2 axle MHV1Minibuses MHV2Car LVMotorcycle MCBicycle UM1Rickshaw/”becak”/pedicab UM2Tricycles UM3Pushcart UM4

Table 4-7. Vehicles Categories for Analysis

In order to look at the detailed behavior of traffic movement with regard to the total flow ofevery stream at the intersection, investigation and measurement are made within 1–minuteand 5–minute intervals for traffic flow and speed of the intersection. In addition, flows ofevery types of vehicle from every stream were also measured. Each of the stream flows ismeasured based on each direction of flow [C – A (1), C – B (2), B – C (3), B – A (4), A –C (5), A – B (6)] as can be seen in Figure 4-19.


51

Since there are no actual passenger car units (PCUs) of every type of vehicle in the actualsituation of the intersection, the traffic flow measurements are based on a number of vehicleswithin a certain time, e.g. veh/1–minute or veh/5–minutes. In this study, the traffic flows wereanalyzed within actual 2 (two) hours (120 minutes). However, a further analysis has shownthat the flow remains stable. Therefore, only 1 (one) hour data is used for analysis and therewould be 60–time group (1–minute) and 12–time group (5–minutes).

Investigations and measurements have been conducted at 10 (ten) three–leg intersections withvarious numbers of types of vehicles and large differences in composition as can be seen inTable 4-8 to Table 4-11 below. In general, each of the intersections has almost performed thesame proportion of vehicles where motorcycles would have the highest portion compared toothers. It was requested to have a bigger portion than 1.0% for each type of vehicle in orderto use it for the further analysis (e.g. regression).

Intersection/Traffic Composition [veh/h]Type ofVehicle 1 2 3 4 5

Truck 3 axle 20 1 N.A N.A N.ATruck 2 axle 212 75 48 29 44Minibuses 10 2 21 1 17Car 819 544 357 240 876Motorcycle 3482 4129 4262 3285 6084Bicycle 58 136 199 161 196Becak (Rickshaw) 24 36 10 1 19Tricycles N.A 4 2 3 3Pushcart (2-wheels) 1 1 3 4 1

4626 4928 4902 3724 7240Table 4-8. Traffic Composition of Intersection–1 to Intersection–5 [veh/h]

Intersection/Traffic Composition [veh/h]Type ofVehicle 6 7 8 9 10

Truck 3 axle 1 N.A N.A N.A N.ATruck 2 axle 39 38 10 20 13Minibuses 6 10 7 2 N.ACar 789 384 157 142 547Motorcycle 4186 3030 2056 2107 1525Bicycle 113 212 112 168 64Becak (Rickshaw) 36 59 13 12 3Tricycles 2 N.A 1 1 4Pushcart (2-wheels) 2 1 2 1 2

5173 3734 2358 2453 2158Table 4-9. Traffic Composition of Intersection–6 to Intersection–10 [veh/h]


52

Intersection/Traffic Composition [%]Type ofVehicle 1 2 3 4 5

Truck 3 axle 0.4 0.0 N.A N.A N.ATruck 2 axle 4.6 1.5 1.0 0.8 0.6Minibuses 0.2 0.0 0.4 0.0 0.2Car 17.7 11.0 7.3 6.4 12.1Motorcycle 75.3 83.8 86.9 88.2 84.0Bicycle 1.3 2.8 4.1 4.3 2.7Becak (Rickshaw) 0.5 0.7 0.2 0.0 0.3Tricycles N.A 0.1 0.0 0.1 0.0Pushcart (2-wheels) 0.0 0.0 0.1 0.1 0.0

Table 4-10. Traffic Composition of Intersection–1 to Intersection–5 [%]

Intersection/Traffic Composition [%]Type ofVehicle 6 7 8 9 10

Truck 3 axle 0.0 N.A N.A N.A N.ATruck 2 axle 0.8 1.0 0.4 0.8 0.6Minibuses 0.1 0.3 0.3 0.1 N.ACar 15.2 10.3 6.7 5.8 25.3Motorcycle 80.9 81.1 87.2 85.9 70.7Bicycle 2.2 5.7 4.7 6.8 3.0Becak (Rickshaw) 0.7 1.6 0.6 0.5 0.1Tricycles 0.0 N.A 0.0 0.0 0.2Pushcart (2-wheels) 0.0 0.0 0.1 0.0 0.1

Table 4-11. Traffic Composition of Intersection–6 to Intersection–10 [%]

4.5 Passenger Car Units (PCUs)

The traffic flow on any given section of road is composed of vehicles of different types,which have all different road–space requirements due to their respective size and performancecharacteristics. In order to allow this in highway capacity measurements, traffic volumes areexpressed in passenger car units (PCUs) which represent the equivalent traffic impedancevalues of various types of vehicle as compared with a value of unity for the passenger car.This study defined passenger car units as a function of static and dynamic performance ofeach type of vehicle from each stream which is considered to be more realistic. There are twobasic principles which should be applied to the estimation of PCUs values for any of theroadway types identified in capacity analysis procedure :

■ The first principle links the concept of passenger car equivalency to the level of service (LOS) concept.■ The second principle emphasizes the consideration of all factors that contribute to the overall effect of trucks on traffic stream performance.

Standard values for passenger car units which vary according to the type of the road or thelocation are used in European countries and the United States. Previously, the standard from


53

the United Kingdom (U.K.) was thought to be more applicable, especially for Jakarta. Therehas been similarity of vehicle sizes and road networks between Jakarta and cities in England.

However, there are many additional and different types of vehicles operating on the road inIndonesia (e.g. Jakarta), for example various types of public transport, e.g. bajaj with tricyclesand becak/pedicab. Slow–moving vehicles such as the bajaj and becak have createdconsiderable disruption where there is insufficient road space to permit overtaking. Minibuseswhich stop frequently and randomly have similar effects. Therefore, it is necessary to expandthe limited method for PCUs factors from developed countries.

4.5.1 Measurement Methods of Passenger Car Units

WERNER & MORRAL (1976) explained the technique which forms the basis ofequivalencies reported in HRB (1965). The number of passing or overtaking that would beperformed per kilometer of highway was considered in order to calculate the PCE of slowermoving vehicles (truck or bus) by the following expression,

)//()1/(

hcarspassengerobservedNHVN

PCEpc

HVHV =

wherepc = Passenger car or LV [-]HV = HV class, B for buses, and T for trucks [-]Npc = Number of overtakings for LVs overtook LVs [veh]NHV = Number of overtakings for LVs overtook one HV [veh]

HUBER (1982) derived a framework for estimating PCE values for level terrain based on thefact that a truck occupies more space than a single passenger car and therefore reducescapacity. For any given LOS with proportion of trucks, p and proportion of cars, (1 – p), it ispossible to calculate the corresponding flow rates qb and qm. Solving for PCE, the results are

111+⎥

⎦

⎤⎢⎣

⎡−⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

m

b

qq

pPCE

whereqb = Flow rates of basic vehicle [cars/h]qm = Flow rates of mixed traffic [veh/h]

with

( )ih

600.3=iq

whereq = Flow rate of vehicles per hour for either a basic stream (i=b) or

an equivalent mixed stream (i = m) [veh/h]hi = Mean time headway in seconds at flow rate qi [sec]

(4-1)

(4-2)

(4-3)


54

this relationship yields

( ) 11/1 +⎥⎦

⎤⎢⎣

⎡−=

b

m

hh

pPCE

MORALES (1989) has shown that PCE for non–standard vehicles PCEi at intersectionapproaches can be approximated as follows

[ ] 11+

−=

(i)PZ(mix)PCE

fi

or

(mix)S(i)P(mix)S(c)S

PCEff

ffi ⋅

−=

with

(mix)S(c)S

Zf

f(mix) =

Sf (c) = saturation flow for infinite sized platoon with standard vehicles only [cars/h]Sf (mix) = saturation flow for infinite sized platoon with a mixed stream [veh/h]

Alternatively, PCEi can be simply approximated as :

(c)h(i)hPCE

s

si =

hs(i) = saturation headway of vehicle type i [sec]hs(c) = saturation headway of standard vehicle respectively [sec]

4.5.2 Measurement at Passenger Car Units Under Mixed Traffic Flow

Within a mixed traffic situation, where different types of vehicles share the same roadwayspace without any physical segregation, the amount of interaction is expected to change withthe mix characteristics. The most intense interaction among the vehicles appears during peakperiods on urban roads. The common practice to analyze mixed traffic flow is to convert allvehicles into equivalent numbers of passenger car units (PCUs).

Variety of road modes are typical in such developing cities. As a result, delay and accidentproblems are very common on the roads. Vehicles that have maneuver difficulties cause

(4-4)

(4-5)

(4-6)

(4-7)


55

friction to other vehicles in the traffic stream. It is rather difficult to estimate the capacity ofroadway under mixed traffic flow unless different vehicle classes are converted to onecommon unit. The most accepted unit is that of passenger cars. All vehicles of heterogeneoustraffic stream are converted into homogeneous equivalent in terms of passenger car units(PCUs). In general, values for PCUs are derived considering the effects of :

■ lane width■ per cent grade■ heavy vehicles, etc.■ (traffic volume on the road – important variable for measuring interaction on urban roads are overlooked).

CUTHBERT (1983) has promoted an analytical approach to define PCUs factors on surveysof traffic flows at selected locations (in Indonesia) which offer the following conditions :

■ saturated flow for significant periods;■ no end constraints on the link under survey; and■ mix of vehicle types

As the most suitable predictive equation which follows a simpler linear relationship is found

bafP +=

and

( )

⎟⎟⎠

⎞⎜⎜⎝

⎛+

+=

vv

E

sLWf

f3.01

015.01

2

whereP = PCUs factor by mode representing traffic flow by link [-]W = Width of vehicle [m]L = Length of vehicle [m]s = Number of stops [stop/km]v = Average speed of vehicle [km/h]vf = Free–flow speed of traffic on the road [km/h]E = Effective road width (total width for a dual carriageway) [m]a = 15.8b = 0.2

By using this approach, values of passenger car units for each type of vehicles at differentroad sections can be concluded as in Table 4-12 below. This approach represents the trafficunder saturated conditions and number of stops, but it almost difficult to find the situations atevery roads in every city in Indonesia (instead of Jakarta).

(4-8)


56

PCUs Factor Defined by Type of Road and Carriageway *)

A B CType of Vehicle(manual countclassification) dual dual single dual single

Car, taxi 1.0 1.0 1.0 1.0 1.0Truck 1.5 1.5 1.7 1.6 2.3Small truck 1.0 1.0 1.0 1.0 1.0Large bus 1.8 2.0 2.6 2.4 3.3Minibuses 1.3 1.4 1.8 1.7 2.6Opelet 1.0 1.1 1.2 1.2 1.7Three-wheeledvehicles N.A N.A 0.8 0.8 0.9

Motorcycle 0.7 0.6 0.5 0.5 0.4Becak N.A N.A 0.6 0.6 0.5Bicycle N.A N.A 0.5 0.4 0.3

Table 4-12. Jakarta Vehicle Characteristics (CUTHBERT, 1983)

*) Road type A – expressway B – suburban

C – urban

Instead of passenger car units (PCUs), light–vehicle units (LVUs) were determined sincethere is a low frequency of passenger cars. It has been studied that free–flow speed for apassenger car is typically 5 km/h to 10 km/h higher than for an average light vehicle (BANGet al., 1995). The assumptions for the regression analysis were that the speed – flowrelationship is linear and that LVUs therefore could be determined from least–square fits ofspeed – flow data with a different traffic composition :

MCMCMHVMHVLVLVLV QK....QKQKAV ⋅−−⋅−⋅−=where

VLV = Average speed of light vehicle [km/h]A = Constant representing free–flow speed [-]Q = Traffic flow for each vehicle type [veh/5–min]K = Speed reduction effect caused by specific vehicle type [-]

The LVUs were obtained as the ratio between the K–coefficient for a specific vehicle type andfor light vehicles,

LV

MHVMHV K

KLVU =

where

LVUMHV = Light vehicle unit of medium heavy vehicle [-]KMHV = Speed reduction effect of medium heavy vehicle [-]KLV = Speed reduction effect of light vehicle [-]

(4-9)

(4-10)


57

The manual (IHCM, 1997) has adopted another way to measure the PCUs factor by usingparameters of headways of each type of vehicles. The headways have to be measuredaccording to the types of leader and follower. This means, the headways have to be measuredbetween two consecutive vehicles of the same type,

LV

MHVMHV H

HLVU =

whereLVUMHV = Light vehicle unit of medium heavy vehicle [-]HMHV = Headway between an MHV following an MHV [sec]HLV = Headway between an LV following an LV [sec]

This method of passenger car units measurement is based on a so–called vehicle interaction orinteraction between different types of vehicle and light vehicles (LV). While performance ofeach type of vehicles from each of the streams is different according to the number of conflict,therefore, further analysis on actual passenger car units value for each type of vehicles werecalculated. This analysis included all types of vehicles with 5 (five) classes (Table 4-7) and 9(nine) categories (LT, MHV1, MHV2, LV, MC, UM1, UM2, UM3, UM4) and six (6) streams(C – A, C – B, B – C, B – A, A – C, A – B). The general form for speed and flowrelationship or vehicles’ interactions of conflict stream, for example C – A (1) and B – A (2)can be read as :

( ) ( ) ( ) ( )( ) ( ) ( )

( ) ( ) ( )BAUMBAUMCAUMCAUMBAUMBAUM

CAUMCAUMBAMHVBAMHVCAMHVCAMHV

BAMHVBAMHVCAMHVCAMHVBALTBALTCALTCALTCALV

QKQKQKQKQKQK

QKQKQKQKAV

−−−−−−

−−−−−−

−−−−−−−−−

⋅−⋅−⋅−⋅−−⋅−⋅−⋅−⋅−⋅−⋅−=

444433

332222

1111

....

and level of interactions between vehicles as passenger car unit can be written as :

CALV

CALTCALT K

KPCU−

−− = and

BALV

BALTBALT K

KPCU−

−− = and so on

whereVLV-CA = Average speed of light vehicle of stream C – A (1) [km/h]A = Constant representing free–flow speed of stream C – A (1) [-]KLT-CA = Coefficient of speed reduction effect of light truck

of stream C – A (1) [-]QLT-CA = Traffic flow of light truck of stream C – A (1) [veh/h]Ki-j = Coefficient of speed reduction effect of vehicle i of stream j [-]Qi-j = Traffic flow of vehicle i of stream j [veh/h]

CHANDRA & SIKDAR (1999) have developed a PCUs factor for a vehicle type based ondynamic and static vehicle performance and geometric variables. The procedures of analyzingthe capacity calibrates for a specific set of ideal conditions, one of them is that the trafficstream contains only passenger cars. The adjustment factor for the presence of vehicles other

(4-11)

(4-12)

(4-13)


58

than cars is based on PCUs. This adjustment factor correlates with the flow rates of passengercars only and mixed traffic streams that are equivalent in terms of drivers’ perception of thelevel of service (LOS). LOS on a segment of highway is defined in terms of two variables :speed and volume. These two variables alone should be able to explain the relative effect ofindividual vehicles on traffic stream in terms of PCUs.

In a mixed traffic situation where many categories of vehicles share the same roadway spacethe proportion of a particular type of vehicle may vary between 10 percent and 60 percent(CHANDRA et al., 1999). The volume of traffic (in terms of vehicles/hour) does not give theimpression of the congestion on a road unless it is accompanied by its traffic composition.The volume of different vehicle types affects the operational characteristics of a highway indifferent ways and to different degrees. Therefore, the composition of a traffic stream is animportant variable which should be used to define the PCU factor. The PCUs of a vehicletype are taken as given by

ic

ici /AA

/VVPCU =

where

VC = Mean speeds for cars (c) in the traffic stream [km/h]Vi = Mean speeds for vehicles type i in the traffic stream [km/h]AC = respective projected rectangular areas of cars [m2]

on the roadAi = Respective projected rectangular area of vehicle type i [m2]

Although the lanes are marked for motorized four–wheeled vehicles, they can accommodatemore than one small sized vehicle conveniently. Vehicles do not move in lanes due to thepoor lane discipline of many road users. In traffic with lane discipline, the occupancy iscontrolled by the length of a vehicle. However, in the condition where vehicles do not followlanes strictly, then the occupancy is better reflected by area (length and width of a vehicle).

The mean velocity is defined as :

)N

(d)V(naV ij

K

jjiji

11

+= ∑=

where

Vi = Mean speed of vehicle i [km/h]aij = Regression coefficients [-]di = Regression coefficient [-]K = Total number of vehicle categories in traffic stream [-]nj = Number of vehicles of j category passing through

the observation point per unit time [veh]

∑=

=K

jjnN

1[-]

(4-14)

(4-15)


59

Passenger car units of vehicles at unsignalized intersections can also be measured by severalother approaches, e.g. capacity method, service–time method, delay method and trafficbehavior method (IHCM, 1997). The capacity method for the analysis in previous studies usesa multi linear regression by counting the number of every type of vehicles that passed the stopline within a certain time under saturated conditions. The total flow is defined as :

UMUMiMCMCiHVHViLVLViTOTALi PCEQPCEQPCEQPCEQQ ⋅+⋅+⋅+⋅=

whereQTOTALi = Total flow at time slice i [pce/h]QLVi = Light vehicle flow at time slice i [pce/h]QHVi = Heavy vehicle flow at time slice i [pce/h]QMCi = Motorcycle flow at time slice i [pce/h]QUMi = Unmotorized flow at time slice i [pce/h]PCE = Passenger car equivalent of every types of vehicle [-]i = 1,.....N [-]N = Number of slice time [-]

If the PCE of light vehicles assumes to be 1.00, the equations would be

UMUMiMCMCiHVHViTOTALiLVi PCEQPCEQPCEQQQ ⋅−⋅−⋅−=

Passenger car units can also be predicted by analyzing the behavior of vehicles (individualbehavior) at unsignalized intersections based on :

1. Average speed of each vehicle, Vi

2. Average crossing time of each vehicle, CTi

SEKHAR (1999) has investigated that aggregation in speed values has a very significanteffect on the speed of other vehicles. By the assumption that other factors are constant, thespeed of a passenger car is Vc and the speed of vehicle type i, Vi, the PCUs’ factor of vehiclespeed, FUV is described as

c

iV V

VFU =

whereFUV = PCUs factor of vehicle type i [-]Vi = Speed of vehicle type i [km/h]Vc = Speed of car/light vehicle [km/h]

Vehicle–Crossing Time (CT) is defined as the period of time required for a vehicle to passthrough the intersection area/conflict area, without causing any hindrance to the vehicles ofother streams. Since the traffic flow consists of various types of vehicles, fast–moving

(4-16)

(4-17)

(4-18)


60

vehicles and slow–moving vehicles, the crossing time of vehicles will be very widely spreadwith regard to the direction of movement, the geometric design, the number of conflicts aswell as the vehicle types. The crossing time is sampled from an empirical distribution ofvehicle–crossing times from field surveys by considering that flows are free from impedance.If the crossing time before the arrival is more than the free value, it means that vehicles areforced to slow down to avoid collision and a probable conflict.

Results from field studies show that in most cases vehicles flow from each approach were notexactly stopped but moving even at the certain very slow–speed, but in some cases, very smallnumber of vehicles would stop at the certain level of flows for very short time. This mighthappen within conflict points. In general, light vehicles and motorcycles would have less timecompared to others, because they have a smaller projected rectangular area, and therefore,they could move to pass the intersection easier. PCU factors of the crossing time of thevehicles are measured as

c

ii CT

CTFCT =

whereFCTi = PCU factors from crossing time [-]CTi = Crossing time of vehicles i [sec]CTc = Crossing time of cars [sec]

4.5.3 Passenger Car Units Measured by Projected Rectangular Area ofVehicles

Consider the relationship between some measurement of impedance along a roadway and theflow rate along that same roadway for two different traffic streams. The flow – impedancerelationship is shown in Figure 4-8, in which the basic curve represents a stream consistingsolely of basic vehicles (passenger cars) and the mixed curve represents a stream with aproportion of trucks p and basic vehicles (1 – p).

Figure 4-8. Flow – Impedance Relationship (HUBER, 1982)

(4-19)


61

For any given LOS (or impedance) it is possible to calculate corresponding flow rates qB andqM as shown. These flow rates for the basic and mixed streams will produce identicalmeasures of LOS and can then be equated so that qB = (1 – p) qM + pqM (PCE). Solving forPCE, the result is

111+⎥

⎦

⎤⎢⎣

⎡−⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

M

B

qq

pPCE

where

PCE = Passenger Car Equivalent [-]p = Proportion of trucks in mixed traffic flow [-]qB , qM = Flow rate at common LOS for basic and mixed

traffic streams, respectively [veh/h]

Figure 4-9. Typical Sample Calculation of PCUs–Values

Figure 4-10. Scheme of PCUs Analysis by Projected Rectangular Area of Vehicle

Traffic Flow

Lm

WmLc

WcW

Light Vehicle

Light Vehicle

Motorcycle

L

(4-20)


62

The Greenshields model of traffic flow, which assumes a straight–line relationship betweendensity and velocity, is used to develop the interrelationship between the variables speed (u),density (k), and flow rate (q) for steady–state flow as it is shown in Figure 4-11. In asimplified case, mixed traffic is assumed to be made up of only two types of vehicles, basicvehicles with effective length LB, effective width wB and free–flow speed ufB and vehicles iwith effective length Li, effective width Wi and free–flow velocity ufi. The mixed–flow rateis the sum of the flow rate of basic vehicles plus the flow rate of trucks :

MiMBM qqq +=

whereqM = Flow rate of mixed vehicles [veh/h]qMB = Flow rate of basic vehicles within mixed stream [veh/h]qMi = Flow rate vehicles i within mixed traffic [veh/h]

Figure 4-11. Greenshields Model of Traffic Flow

The proportion p of vehicles i in the mixed traffic stream flow is as follows :

M

Mi

qq

p =

The density of the mixed flow as it is modified as a projected rectangular area of the vehicle isthe sum of the density of basic vehicles plus the density of vehicles i :

kM = kMB + kMi

where

kM = Density of mixed vehicles [veh/km]kMB = Density of basic vehicles within mixed stream [veh/km]kMi = Density of vehicle i within mixed stream [veh/km]

(4-22)

(4-23)

(4-21)


63

The proportion p' of vehicles i in the mixed stream projected rectangular area is as follows :

M

Mi

kkp =′

The mean velocity of the mixed stream of traffic is the harmonic mean of the velocities of thebasic vehicles and vehicles i :

( )⎭⎬⎫

⎩⎨⎧

⎥⎦

⎤⎢⎣

⎡ −+⎟⎟

⎠

⎞⎜⎜⎝

⎛=

MBMi

M

up

up

u1

1

whereuM = Mean velocity of mixed traffic stream [km/h]uMB = Mean velocity of basic vehicles within mixed traffic stream [km/h]uMi = Mean velocity of vehicles i within mixed traffic stream [km/h]p = Proportion of vehicles i in mixed traffic stream flow [km/h]

If qo is defined as the optimum flow rate, a relationship could be written as qo = ko · uo ,

OMiOMiOMOMi ukpqq ⋅==

( ) OMBOMBOMOMB ukqpq ⋅=−= 1and

OMi

OM

OMi

OMiOMi u

pquq

k ==

( )OMB

OM

OMB

OMBOMB u

qpuq

k−

==1

Substitute Equation 4-26 and Equation 4-27 into Equation 4-24 yields

( )[ ]( )

( )⎭⎬⎫

⎩⎨⎧

⎥⎦

⎤⎢⎣

⎡+

=

⎟⎟⎠

⎞⎜⎜⎝

⎛

⎭⎬⎫

⎩⎨⎧

⋅⋅⋅−

+

=

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+

=

+=′

OMB

OMi

OMBOM

OMiOM

OMi

OMB

OMBOMi

OMi

puu-p

upquqp

kk

kkkp

11

1

11

1

1

1

(4-24)

(4-25)

(4-26)

(4-27)


64

Based on the Greenshields model of traffic flow uO = uf /2, the final expression is

( )⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ −+

=′

fB

fi

upup

p1

1

1

The jam density kj for basic vehicles becomes,

BjB L

Lk =

and for mixed vehicles,

( )[ ]BijM LpLp

Lk′−+′

=1

where

L = Unit length of roadway [km]Li and LB = Effective length of vehicles i and basic vehicles B [m]

As density (length of vehicles) would be modified as road occupancy (projected rectangulararea of vehicle) which Li and Wi represent length and width of vehicle type i and see alsoFigure 4-10, therefore,

BBjB WL

WLk⋅⋅

=

and for mixed vehicles,

( ) ( ) ( )[ ]BBiijM WLpWLp

WLk⋅′−+⋅′

⋅=

1

When we assume that a flow rate qB of basic vehicles will only produce the same average intravel time t(q)B as is produced by a flow rate qM of mixed vehicles, t(q)B = t(q)M.

For any given length of roadway, this results in equal average velocities for the two or moretraffic streams, as shown in Figure 4-12, uB = uM = u. If we look at the Equation 4-20 andnote that qB = kB u and qM = kM u, it follows that qB/qM = kB/kM. Based on Figure 4-12,

( )fB

fBjBB u

uukk

−=

(4-28)

(4-29a)

(4-29b)

(4-29c)

(4-29d)


65

and( )

fM

fMjMM u

uukk

−=

so( )( )⎥⎥⎦

⎤

⎢⎢⎣

⎡

−

−

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛==

uuuu

kk

uu

kk

qq

fM

fB

jM

jB

fB

fM

M

B

M

B

The general case where ufM < ufB ; kjM < kjB, from Equation 4-20 we have

( )( ) 111

+⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−⎥⎥⎦

⎤

⎢⎢⎣

⎡

−−

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

uuuu

kk

uu

pPCE

fM

fB

jM

jB

fB

fM

Looking at Figure 4-12 and Equation 4-31 there is undefined PCE with u > ufM. For u < ufM

and by using Greenshield model of traffic flow,

( ) ⎥⎦⎤

⎢⎣⎡ −+⎟⎟⎠

⎞⎜⎜⎝

⎛== 2

111

2y

uuu fB

MB

where

OB

B

qqy =

Figure 4-12. Determination of PCE–values Figure 4-13. Determination of PCE–values by Equal Travel Time by Equal Total Travel Time

(HUBER, 1982) (HUBER, 1982)

If we assume a flow rate qB of basic vehicles only, it will produce the same total travel timeT(q)B as is produced by a flow rate qM of mixed vehicles, so that T(q)B = T(q)M.

At any given length of roadway, this is equivalent to equal vehicle hours of occupancy perhour for the two traffic streams, and since T(q) is numerically equal to density k, kB = kM = k,

(4-30)

(4-31)


66

as shown in Figure 4-13. From Equation 5-8 and noting that qB = k uB and qM = k uM, itfollows that qB/qM = uB/uM .

As we can see in Figure 4-13 and using similar triangles,

( )jB

jBfBB k

kkuu

−=

and( )

jM

jMfMM k

kkuu

−=

so that( )( )⎥⎥⎦

⎤

⎢⎢⎣

⎡

−−

⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛==

kkkk

kk

uu

uu

qq

jM

jB

jB

jM

fM

fB

M

B

M

B

In general cases where ufM < ufB ; kjM < kjB, and from Equation 4-20 :

( ) ( )[ ] 111+

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−−÷−⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛= kkkk

kk

uu

pPCE jMjB

jB

jM

fM

fB

Studies from HUBER (1982) have defined the mixed traffic stream that consists of twocomponent steady–state flows. The first component are the basic vehicles within the mixedstream with ufMB (= ufB) and kjMB [= (1 – p') kjM] and the second component are trucks withinthe mixed traffic stream with ufMT (= ufT) and kjMT (= p' kjM).

Figure 4-14. Speed – Density Relationships for Mixed Flow and Two Component Flow (HUBER, 1982)

(4-32)

(4-33)


67

By the Greenshields relationship of speed – density from Figure 4-14,

( ) ⎥⎦⎤

⎢⎣⎡ −+⎟⎟⎠

⎞⎜⎜⎝

⎛= 2

111

2 MBfMB

MB yu

u

and

( ) ⎥⎦⎤

⎢⎣⎡ −+⎟⎟⎠

⎞⎜⎜⎝

⎛= 2

111

2 MTfMT

MT yu

u

where

( )( ) MT

OM

M

OM

MMB y

qq

qpqpy ==

−−

=11

Figure 4-15. PCE–values by Equal Basic Vehicle Travel Time (HUBER,1982)

St. JOHN (1976) has used mean speed of basic vehicles as the criterion for determining PCE–values. A diagram of the situation is shown in Figure 4-15 where ufMB = ufB and uB = uMB

= u. By Equation 4-20,

111+⎥

⎦

⎤⎢⎣

⎡−⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

M

B

qq

pPCE

where

( ) ( ) BBMBMB

M ukq;p

ukp

qq =

−=

−=

11so that

( )MB

B

B

M

kkp

qq −

=1

For similar triangles in Figure 4-15,

( ) ( )fB

fBjBB

fB

fBjMBMB u

uukk

uuuk

k−

=−

= ;


68

so that( )

jMB

jB

B

M

kkp

qq −

=1

and

( ) ( )( ) ⎟

⎟⎠

⎞⎜⎜⎝

⎛⎥⎦

⎤⎢⎣

⎡′−

−=′−=

jM

jB

B

MjMjMB k

kpp

qqandkpk

111

From Equation 4-29a and Equation 4-29b,

( )[ ] ( )[ ]B

BTBT

BjM

jB

LLpLp

LLpLp

LL

kk ′−+′

=⎭⎬⎫

⎩⎨⎧ ′−+′⎟⎟⎠

⎞⎜⎜⎝

⎛=

11

Substituting in Equation 4-34 gives

( ) ( ) ( )

B

BT

M

B

L

LLp

pp

qq ⎭

⎬⎫

⎩⎨⎧

+⎥⎦

⎤⎢⎣

⎡′−

′−

=1

1

From Equation 4-28 in which ufi is represented free–flow speed of truck ufT,

( )[ ] ( )[ ] ( ){ } ( ) fT

fBfTfTfB

fTfB

fB

upup

upupupupup

up)p(

p−

=−÷−+⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

−+=

′−′

111

11

substituting in Equation 4-35,

( )pLL

uup

qq

B

T

fT

fB

M

B −+⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛= 1

So that by Equation 4-20,

⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=+⎥

⎦

⎤⎢⎣

⎡−⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

B

T

fT

fB

M

B

LL

uu

qq

pPCE 111

When we consider Equation 4-29c and 4-29d as a modification from density to vehicles’occupancy, this follows

( ) ( )( )[ ] ( ) ( )( )[ ]BB

BBiiBBii

BBjM

jB

WLWLpWLp

WLWLpWLp

WLWL

kk

⋅⋅′−+⋅′

=⎭⎬⎫

⎩⎨⎧

⋅⋅′−+⋅′

⎟⎟⎠

⎞⎜⎜⎝

⎛⋅⋅

=11

(4-34)

(4-35)


69

Substituting Equation 4-34,

( ) ( ) ( ) ( )[ ]

BB

BBii

M

B

WL

WLWLp

pp

qq

⋅⎭⎬⎫

⎩⎨⎧

⋅+⋅⎥⎦

⎤⎢⎣

⎡′−

′−

=1

1

From Equation 4-28 and substituting in Equation 4-36,

( )( ) ( )p

WLWL

upu

qq

BB

ii

fi

fB

M

B −+⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡⋅⋅

⎥⎥⎦

⎤

⎢⎢⎣

⎡= 1

From Equation 4-20,

( )( )⎥⎦

⎤⎢⎣

⎡⋅⋅

⎟⎟⎠

⎞⎜⎜⎝

⎛=+⎥

⎦

⎤⎢⎣

⎡−⎟⎟

⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

BB

ii

fi

fB

M

B

WLWL

uu

qq

pPCE 111

( )( ) ⎥⎦

⎤⎢⎣

⎡⋅⋅

⎟⎟⎠

⎞⎜⎜⎝

⎛

=

ii

BB

fi

fB

WLWL

uu

PCE

or

( )( ) ⎥⎦

⎤⎢⎣

⎡⋅⋅

⎟⎟⎠

⎞⎜⎜⎝

⎛

=

ii

BB

OMi

OMB

WLWL

uu

PCE

whereuOMB = Optimum speed of basic vehicles within mixed traffic stream [km/h]uOMi = Optimum speed of vehicle type i within mixed traffic stream [km/h]LB = Effective length of basic vehicles [m]WB = Effective width of basic vehicles [m]Li = Effective length of vehicle type i [m]Wi = Effective width of vehicle type i [m]

This recent formula for passenger car units (Equation 4-37) is a modified approach of whathas been found out by St. JOHN (1976) which considered only an effective length of vehicles.This modification approach considered transversal and longitudinal dimensions (length andwidth) of vehicles. The dimension is very important as long as the concept of lane disciplinedoes not exist. Instead of the acceptance (widths) concept, consisting flow of various types ofvehicles with various dimensions/projected area is considered. This concept is rather similarto previous formula (Equation 4-14) which also considered similar cases of mixed traffic

(4-36)

(4-37)


70

flow. Since the study has difficulties to find the free–flow speed of each type of vehicle, uf,the optimum speed was used, uO or the average speed of each type of vehicle.

Results have been found for each type of vehicle from each stream based on vehicleinteraction and the projected rectangular area which can also be compared with formal valuesfrom the manual (IHCM, 1997) for the case of intersection–1; see Table 4-13 and Table 4-14.However, in most of the cases it was found that the total number of each vehicle has notreached more than 1% from the total number of all vehicles (e.g. trucks, minibuses andunmotorized vehicles) and it was also found from the measurements that the relationship andinteractions between vehicle streams did not meet the standard relationship as in Equation4-9, therefore, some of the PCU values based on the method of vehicle interactions could notbe measured properly and would not be used for further analysis. Alternatively, an approachof passenger car unit measurement (projected rectangular area of vehicles) which is based onvehicles’ performance were considered to be more appropriate.

PCUs Based on Vehicle InteractionType ofVehicle C – A C – B B – C B – A A – C A – B

IHCM

Truck3 axle N.A N.A N.A N.A N.A N.A

Truck2 axle 3.4 0.4 0.7 N.A 0.8 2.4

Minibuses N.A N.A N.A N.A N.A N.A

1.3

Car 1.0 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.9 0.7 2.0 1.7 0.3 0.7 0.5Bicycle N.A N.A N.A N.A N.A N.ABecak(Rickshaw) N.A N.A N.A N.A N.A N.A

Tricycles N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A

0.3

Table 4-13. PCUs Based on Method of Vehicle Interaction and IHCM

PCUs Based on Vehicle Speed and Projected RectangularType ofVehicle C – A C – B B – C B – A A – C A – B

Truck 3 axle 2.7 N.A N.A N.A 2.7 N.ATruck 2 axle 2.8 2.8 2.9 2.8 2.6 3.1Minibuses 1.6 N.A N.A N.A 2.0 1.6Car 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.1 0.2 0.2 0.1Bicycle 0.3 0.2 0.2 0.2 0.2 0.2Becak(Rickshaw) 0.7 0.8 0.6 0.7 0.6 0.5

Tricycles N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A 1.0

Table 4-14. PCUs of Each Type of Vehicle of Each Stream


71

Investigations on each of the intersections have been carried out for their PCU values. Basedon the results, we have an identical value of each type of vehicle from each stream and thevalues remain the same for each vehicle from each stream, especially with a digit of 1/10(0.1), therefore, those PCU values can be resumed as an average value of PCUs of each typeof vehicle from each stream. This can be seen in Table 4-15. All intersections are presented inAppendix C.

Intersection/PCUsType ofVehicle 1 2 3 4 5 6 7 8 9 10

Truck3 axle 2.7 5.3 N.A N.A N.A 2.5 N.A N.A N.A N.A

Truck2 axle 2.8 2.7 3.3 2.5 3.1 2.8 3.5 2.8 3.3 2.5

Minibuses 1.7 2.1 1.4 1.9 1.6 1.8 1.2 2.3 3.0 N.ACar 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.2Bicycle 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3Becak(Rickshaw) 0.7 0.6 0.5 0.7 0.6 0.6 0.6 0.5 0.6 1.0

Tricycles N.A 0.5 1.0 0.3 0.5 0.4 N.A 0.4 0.5 0.6Pushcart(2-wheels) 1.0 1.0 0.9 0.8 1.4 0.9 1.2 1.2 0.7 1.7

Table 4-15. Average PCUs of Each Vehicle at Each of Intersection

By using current values for PCUs of each vehicle of each intersection (Table 4-15), furtherstudies and analysis of flow and capacity with passenger car units (PCUs) per hour (pcu/h)can be applied and the total flow of each intersection in passenger car units can be resumedas at Table 4-16 and Table 4-17. The intersections described here performed a number ofvehicles in a range of 438.4 pcu/h to 2209.4 pcu/h.

Intersection/Flow [pcu/h]Type ofVehicle 1 2 3 4 5

Truck 3 axle 54.0 5.3 N.A N.A N.ATruck 2 axle 593.6 202.5 158.4 72.5 136.4Minibuses 17.0 4.2 29.4 1.9 27.2Car 819.0 544.0 357.0 240 876.0Motorcycle 696.4 825.8 426.2 328.5 608.4Bicycle 11.6 27.2 39.8 32.3 39.2Becak (Rickshaw) 16.8 21.6 5.0 0.7 11.4Tricycles N.A 2.0 2.0 0.9 1.5Pushcart (2-wheels) 1.0 1.0 2.7 3.2 1.4

2209.4 1633.8 1020.5 679.9 1701.5Table 4-16. Traffic Composition of Intersection–1 to Intersection–5


72

Intersection/Flow [pcu/h]Type ofVehicle 6 7 8 9 10

Truck 3 axle 2.50 N.A N.A N.A N.ATruck 2 axle 109.2 106.4 28.0 66.0 32.5Minibuses 10.8 12.0 16.1 6.0 N.ACar 789.0 384.0 157.0 142.0 547.0Motorcycle 418.5 606.0 205.6 210.7 305.0Bicycle 22.6 42.4 22.4 33.6 19.2Becak (Rickshaw) 21.6 35.4 6.5 7.2 3.0Tricycles 0.8 N.A 0.4 0.5 2.4Pushcart (2-wheels) 1.8 1.2 2.4 0.7 3.4

1375.0 1187.4 438.4 466.7 912.5Table 4-17. Traffic Composition of Intersection–6 to Intersection–10

4.6 Traffic Flow Performance

Observation and measurement at ten (10) three–leg intersections instead of fourteen (14) havebeen done. The locations are taken in the city of Pontianak, West Kalimantan. Eachintersection has been monitored during the peak hour time period, e.g. morning period (06.00– 08.00 am) and evening period (16.00 – 18.00 pm. The total period of time observation foreach intersection is two (2) hours (120 minutes), however, the results show that in two hoursthe flow fluctuation remains the same. Therefore, it was decided to take investigations andmeasurements of one hour for each intersection. The total flow of each intersection is in arange of 2158 veh/h – 7240 veh/h (Table 4-8 and Table 4-9) with the largest percentage ofmotorcycles (MC) of 70% – 88%. The percentage of cars (LV) was in a range of 5.8% –25.3% (Table 4-10 and Table 4-11). Using values of PCUs as it was mentioned before, we cansee the total flow of each intersection in vehicles per hour (veh/h) and passenger car units perhour (pcu/h), see Figure 4-16.

Figure 4-16. Total Flow of Each Intersection

10987654321

Intersection

8000

7500

7000

6500

6000

5500

5000

4500

4000

3500

3000

2500

2000

1500

1000

500

0

Flow

1232

,3

915,

4

1113

,81416

,7

2203

,62645

,4

1232

,41753

,0

1983

,2

2231

,1

2158

,0

2453

,0

2358

,0

3734

,0

5174

,0

7240

,0

3724

,0

4902

,0

4928

,0

4626

,0

TOTAL FLOW OF EACH OF INTERSECTION

Flow [pcu/h]Flow [veh/h]

TYPE OF MEASUREMENT


73

In order to have detailed information, observations of flow and speed were made in 1–minute and 5–minute intervals. Typical flows (number of vehicles) in 1–minute intervals canbe seen in Figure 4-17. The figure shows small differences of vehicles’ flow in the 1–minuteinterval time. Further details for flows of each intersection can be seen in Appendix A.

Figure 4-17. Traffic Flow of Intersection a 1–minute interval

Observation was also made in a 5–minute interval in order to have a different view of the flowand speed performance. Figure 4-18 shows difference of vehicles between interval which islikely in a range of 50 vehicles, lower or higher. Two figures of the traffic flow with twodifferent interval time investigations did not show a significant/high difference flow in everyminute and every 5–minutes (intersection–1) while a significant number of vehicles wasrequired as a potential value (maximum flow) to measure the capacity. Therefore, the studyfound difficulties to analyze the maximum flow (capacity) of intersection due to theintersections which did not reach its maximum flow (capacity) during the two hours of theinvestigation. The study required to consider other sources and studies relating to the criticalspeed at the intersection while the maximum flow (capacity) of streams have reached. Thecritical speed is defined as the average speed of streams while the capacity or the maximumflow at the intersection is reached. It is assumed that the capacity of stream is reached at thecritical speed while other streams might not have an opportunity to pass through the

5957555351494745434139373533312927252321191715131197531

1-minute interval

120

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

Flow (veh/1-minute interval)


74

intersection (volume = 0.0). Furthermore, this critical speed will be used to measure thecapacity based on the new model which will be discussed further in the next chapter.

Figure 4-18. Traffic Flow of Intersection a 5–minute interval

This study required counted flows of each stream (6 streams) at intersections, therefore, thefollowing scheme of three–leg unsignalized intersections was constructed for simplification offurther calculation. Leg A and leg C are treated as the major road because most of the trafficflows from those legs were larger than others without any implication to the priority rule. Sixstreams were defined as C – A, C – B, B – C, B – A, A – C and A – B (Figure 4-19).Observations of flows in 1–minute and 5–minute intervals for all intersections are presentedin Appendix A. Flows of each stream was observed during two hours. But only one hour wasused for analysis, because it was found that each stream of intersections performed almost thesame number of vehicles (flow) in both hours. Results from the observation (Appendix A)showed that the number of vehicles (flow) from legs C and A are higher than from legs A andC, especially at intersection–1. Only at intersection–10 remain have lower numbers ofvehicles. Thus, we say that leg A and leg C are called the major road and leg B is the minorroad, a definition which is required for capacity analysis based on the Indonesian manual.

121110987654321

5-minute interval

500

450

400

350

300

250

200

150

100

50

0

Flow

(veh

/5-m

inut

e)

355

403394

417

366

386

404387

374

395

355

390

Flow (veh/5-minute interval)


75

Figure 4-19. Typical Lay–Out of Three–Leg Intersection

Figure 4-20. Flow of Each Stream (intersection–8)


A - BA - CB - AB - CC - BC - A

Direction of flow

800

600

400

200

0

Flow

(veh

/h)

166

421

179

694

443455

Flow of each stream


Direction of flow

200,0

150,0

100,0

50,0

0,0

Flo

w (

pcu

/h)

54,5

63,757,7

119,7

74,368,5

Flow of each stream


Di ti f fl

800

700

600

500

400

300

200

100

0

Flow

(veh

/h)

188

279

439

563

530

159

Flow of each stream


Direction of flow

300,0

250,0

200,0

150,0

100,0

50,0

0,0

Flow

(pcu

/h)

73,4

117,9

188,7

233,1241,0

58,4

Flow of each stream

C-A

C-B

A-C

A-B

B-C

B-A

Arm C(Major)

Arm A(Major)

Arm B(Minor)


76


Based on the observed traffic composition at the intersections, the traffic flow consists ofmotorized and unmotorized vehicles which were defined as fast–moving vehicles andslow–moving vehicles according to their dynamic characteristics. SOEGIJOKO & HORTHY(1991) have classified various types of vehicles according to their static and dynamicdimensions. The types of vehicles and their dimensions of speed at the road sections are asfollows :

Type of VehicleLength ofVehicle

[m]

Lane WidthOccupied

[m]

CruisingSpeed[km/h]

AverageMaximum Speed

[km/h]Truck 7.50 2.35 N.A 80Minibuses 5.40 1.90 60 80Car 4.05 1.60 100 130Motorcycle 1.60 0.80 80 80Bicycle 1.75 0.60 16 30Becak (Rickshaw) 2.25 1.00 10 25Pushcart 2.10 0.80 5 N.A

Table 4-18. Static and Dynamic Characteristic of Vehicles (SOEGIJOKO et al., 1991)

The number of motorcycles of each intersection dominated the others (70% – 88%) but wouldhave the same percentage of cars (light vehicle) in passenger car units (see Figure 4-23). Bothhave a significant impact on traffic flow movement at intersections, especially when weconsider the area of intersection and width of road. There are more opportunities formotorcycles to occupy and pass through the intersection compare to other motorized vehicles,e.g. cars would occupy a larger area of the intersection.


Direction of flow

1.600

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

748

1.300

571

480

321

1.207

Flow of each stream


Direction of flow

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flow

(pcu

/h)

350,2

639,2

272,2

222,3

168,2

570,3

Flow of each stream


77

Figure 4-23. Typical Flow of Each Type of Vehicle (intersection–1)

4.7 Mean Speed Performance

Instead of flow observations, the study has also conducted speed observations for each type ofvehicle from each stream. Camcorders were used to monitor the speed. Therefore, the studycould only measure the average speed of vehicles which was counted from the departure tothe arrival. There are known definitions of speed, e.g. average running speed (space meanspeed), average travel speed (space mean speed), u and time mean speed, v. The time meanspeed (spot speed) is defined as the arithmetic mean of all instantaneous vehicle speeds at agiven ”spot” on a roadway section and the space mean speed, u is defined as the mean travelspeed of vehicles transversing a roadway segment of a known distance, l

∑=

=n

iiv

nu

1

1

and

i

ii t

lv =

whereu = Space mean speed [km/h]vi = Time mean speed of vehicle type i [km/h]n = Number of vehicles passed a road segment i, li [-]

Due to a large variation in speeds for different categories of vehicles, the spot speed and spacemean speed as normally calculated for homogeneous traffic cannot be considered for mixedtraffic (CHANDRA et al., 2001). Many researchers suggested the use of weighted space meanspeed or mean stream speed. To find the mean stream speed, a trap of suitable length ismarked on the road and the speed of each category of vehicles considered for counting iscalculated. The weighted average mean speed is given by

∑

∑

=

== k

ii

k

iii

m

n

vnv

1

1

Pushcart 2 wheels

RickshawBicycleMotorcycleCarMinibusTruck 2 axles

Truck 3 axles

Type of vehicle

4.000

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

12458

3.483

819

10

212

20

Flow of each type of vehicle

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1000,0

900,0

800,0

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flow

(pcu

/h)

9,016,817,4

696,6

819,0

16,0

593,6

54,0


(4-38)

(4-39)


78

where

k = Total number of vehicle categories present in stream [-]vm = Mean stream speed [km/h]vi = Speed of vehicle of category i [km/h]ni = Number of vehicles of category i [-]

This study concerns the speed at intersections, because it was found that during theobservation most of the vehicles maintained their speed (decelerate) without stop. E.g. datafrom intersection–5 with 7240 vehicles per hour have only less than 0.5% of stoppedvehicles. Concluding from data of all intersections, vehicles tend to decelerate whileapproaching the intersection without stopping (98% traffic flow) while the rest have to waitin average less than 2 seconds (gap is less than 2 seconds), therefore, this study has not takeninto account stops of vehicles.

Highway capacity, in a broad sense, is a measure of the effectiveness of the highway inaccommodating traffic. The development of macroscopic traffic flow models (speed – flow,speed – density and flow – density) under mixed traffic conditions is essential to theformulation of rational and practical values of capacity under such traffic conditions.RAMANAYYA (1988) has conducted studies in heterogeneous traffic flow in India. Thetraffic has been categorized as fast–moving vehicles (e.g. cars, buses, trucks, auto–rickshawand scooter) and slow–moving vehicles which include bicycles, cycle–rickshaws and bullockcarts. He developed a model of speed and flow relationship based on computer simulationwith different traffic volumes and a varying percentage mix of the different types of vehicleswhich make up the stream. The simulation runs with traffic volumes varying from 100 veh/hto 1200 veh/h, and varying number of slow–moving vehicles with 10%, 30% and 50%. Thefollowing relationship indicated the relationship between speed, flow, density and percentageof slow–moving vehicles.

The general relationship was found as :PKV ⋅−⋅−= 49.28log38.756.42

PQV ⋅−⋅−= 95.36log26.98.59

PeQ K ⋅−⋅+= 9.49239.22053.178 log

where

V = Average speed of the traffic stream [km/h]Q = Average traffic flow [veh/h]K = Average traffic density [veh/km]P = Percentage of slow–moving vehicles [%]

In order to study mixed traffic in a comprehensive manner, speed models must be developedon the basis of these results which help in understanding the problems of interactions between


79

vehicles in the stream. In addition, it was possible to derive expressions for the average speedof each class of vehicles :

PQVCAR ⋅−⋅−= 38.30log48.2142.101PQVBUS ⋅−⋅−= 39.30log75.2012.95PQVTRUCK ⋅−⋅−= 61.19log81.218.92PQVAUTO ⋅−⋅−= 02.30log35.180.85

PQV FMOTORCYCLE ⋅−⋅−= 41.23log7.169.85 2

In general, the form of relationship between speed, flow, density and percentage of slow–moving vehicle can be concluded as

PKQKAV

PKQKAV

PKQKAVPKDKAV

pQii

PQLVLV

PQ

PD

⋅−⋅−=

⋅−⋅−=

⋅−⋅−=⋅−⋅−=

log

log

loglog

whereV = Average speed of the traffic stream [km/h]VLV = Average speed of light vehicle [km/h]Vi = Average speed of vehicle type i [km/h]A = Constant representing free–flow speed [-]ALV = Constant representing free–flow speed of light vehicle [-]Ai = Constant representing free–flow speed of vehicle type i [-]KD = Speed reduction effect caused by traffic density [-]KQ = Speed reduction effect caused by traffic flow [-]KP = Speed reduction effect caused by slow–moving vehicles [-]P = Percentage of slow–moving vehicles [%]D = Average traffic density [veh/km]

A previous study from BANG et al. (1995) which is used as the basic consideration for theIINDONESIAN HIGHWAY CAPACITY MANUAL (1997) derived the speed and flowrelationship from empirical data as

MCMCMHVMHVLVLVLV QK....QKQKAV ⋅−−⋅−⋅−=

whereVLV = Speed of light vehicle [km/h]A = Constant representing free–flow speed of light vehicle [-]Q = Traffic flow for each vehicle type [veh/5–min]K = Speed reduction effect caused by specific vehicle type [-]

One of the concerns in studying uncontrolled intersections has always been the development(hierarchy) of priority regulations together with a comprehensive set of rules which ought toenable the determination of the most adequate solution for each intersection depending on itsgeometry and traffic characteristics. Formally, three–leg and four–leg intersections inIndonesia are managed by the common rule of ”priority–to–the–left” (IHCM, 1997).

(4-40)


80

However, drivers’ behavior during their approach to the intersection do not fulfil thisrequirement. Drivers are found more aggressive while approaching intersections and drivers’understanding of the rule is very poor and, perhaps because of that, in most of the cases theintersections seem not to be working correctly from the rule point of view.

Speed distributions of every type/group of vehicles at the intersection could be the mostimportant characteristic to be measured. Based on the assumption that speed or crossing timewill be affected by the number of interactions between flow streams and impacts of thenumber of vehicle types at the intersection, and the number of conflicts. By using the timecode device and field measurements recorded with camcorders, the real speed of every type ofvehicle at the intersection can be measured. Technically, the speeds were measured based onarrival and departure time of every type of vehicle (as they are recorded) and each distance ofevery direction could also be measured using the given line references at the intersection.

Speeds of every vehicle type and flow directions were measured and the average speed wascalculated based on the 1–minute and 5–minute interval of traffic movement. The speed ismeasured based on travel time of each vehicle k, e.g. tCA passing through the intersection at acertain length, e.g. distance tC – tA or tC – tB. In order to find the time of departure tC andarrival tA or tB, line references were made on each leg of the intersections (A, B, C) and anaverage distance/path of tC – tB is considered. The reference lines with white color were madevisable enough to be seen from the video during observation, see Figure 4-24.

Figure 4-24. Scheme of Speed and Flow Measurement at Intersection

The measurements were conducted at each intersection and each type of vehicle. The resultscan be seen in Table 4-19 (e.g. intersection–1) with performance of average speed and

tCA

kC

tC

Direction of vehicle travel

A, B, C Arrival and departure line of vehicle at intersection Time arrival at line C : Vehicle k arrived at line C

C

B

A

tC tA

tB

kC kA

kB

tCA

Total travel time at distance C and A Vehicle k arrived at time tC


81

maximum speed of vehicles at the intersection. However, since each type of vehicle from thestreams has had a different movement behavior, the speed of each stream has to be measuredseparately while the speed of each vehicle from all traffic streams was also measured (alldirections : C – A, C – B, B – C, B – A, A – B, A – C. Table 4-20 and Figure 4-26 showdifferent speeds of each stream and each type of vehicle. The data from intersection–1 aretypical also for other intersections (Appendix C). In general, the speeds of vehicles from the”major road” (C – A and A – C) are higher than for other streams due to the straight pathwith less conflicts. Vehicles from ”major roads” (streams: C – A and A – C) were found to bemore aggressive, because the flow is found higher than others and the streams are consideredto be more frequent. From the tables (Table 4-19, Table 4-20 and Appendix C) we can seethat motorcycles were found to be faster than others due to their small projected rectangulararea. Motorcycle have high engine displacement (e.g. 125 cc) with average maximum cruisingspeed of 80 km/h (SOEGIJOKO et al., 1991), therefore, motorcycles would have a betteropportunity to pass through the intersection and complete their travelling in a short period oftime. Also bicycles would almost be in the same situation while they have the opportunity tocomplete their travelling without any obstructions, due to their small projected rectangulararea.

Type of Vehicles Average Speed[km/h]

Maximum Speed[km/h]

Truck 3 axle 24.76 33.70Truck 2 axle 19.54 32.60Minibuses 20.66 29.30Car 21.14 29.68Motorcycle 25.81 34.62Bicycle 15.12 31.70Becak (Rickshaw) 11.28 16.70Tricycles N.A N.APushcart (2-wheels) 5.40 5.40

Table 4-19. Average Speed of Vehicles at Intersection

StreamsC – A C – B B – C B – A A – C A – BType of Vehicle

Speed [km/h]Truck 3 axle 27.0 N.A N.A N.A 21.4 N.ATruck 2 axle 26.5 11.2 10.9 16.6 22.1 18.7Minibuses 27.1 N.A N.A N.A 17.7 21.4Car 27.6 11.6 11.9 17.6 21.9 21.4Motorcycle 31.8 14.9 17.9 22.6 25.9 28.2Bicycle 16.4 9.3 12.1 12.8 17.3 17.6Becak (Rickshaw) 13.2 5.0 7.3 8.4 13.4 13.8Tricycles N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A 5.4

Mean Speed [km/h]30.6 14.0 16.6 20.8 24.9 26.2

Table 4-20. Average Speed of Vehicles at Each Stream


82

Figure 4-25. Typical Mean Speed of Each Stream (intersection–1)

Figure 4-26. Typical Mean Speed of Each Vehicle at Each Stream (intersection–1)

The typical speed performance shown in Figure 4-25 is the result of one intersection whileothers remain in the same proportion based on their direction of stream (see also Appendix A)while the average speed for every type of vehicle from each stream is shown in Figure 4-26.The speed range collected from all intersections can be resumed in Table 4-21 while detailscan be found in Appendix C.

5957555351494745434139373533312927252321191715131197531

1 - minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

Speed Performance of Each Stream

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

Speed Performance of Each Type of Vehicle


83

Stream Range Average Speed[km/h]

Maximum 30.6C – A

Minimum 12.74Maximum 20.30

C – BMinimum 14.00Maximum 14.97

B – CMinimum 9.10Maximum 22.56

B – AMinimum 10.78Maximum 25.00

A – CMinimum 10.93Maximum 35.60

A – BMinimum 10.00

Table 4-21. Speed Performance of Each Stream Flow of Intersections

Every streams’ speed seems to have a normal distribution and small deviation while themeasurement contents all types of vehicles with different flow rates and speed range. Figure4-27 to 4-32 show the indication. The figures represent normal distribution of speed ofstreams at intersections and this finding is of significance for further work on the relationshipbetween speed and flow.

Figure 4-27. Speed Distribution of Stream Figure 4-28. Speed Distribution of Stream C – A (1) C – B (2)

42,540,037,535,032,530,027,525,022,520,017,515,012,510,07,55,02,50,0

Speed (km/h)

200

190

180

170

160

150

140

130

120

110

100

90

80

70

60

50

40

30

20

10

0

Freq

uenc

y

Mean =30,596�Std. Dev. =2,5196�

N =1.207

Flow direction C - A

30,027,525,022,520,017,515,012,510,07,55,02,50,0

Speed (km/h)

50

40

30

20

10

0

Freq

uenc

y

Mean =14,043�Std. Dev. =2,3475�

N =321

Flow direction C - B


84

Figure 4-29. Speed Distribution of Stream Figure 4-30. Speed Distribution of Stream B – C (3) B – A (4)

Figure 4-31. Speed Distribution of Stream Figure 4-32. Speed Distribution of Stream A – C (5) A – B (6)

It was expected to find a speed distribution of each stream in order to get a correlation withthe flow of each conflict stream. Both parameters are very important for further analysis ofthe maximum flow (capacity) of an intersection corresponding with the speed – flowrelationship of conflict streams. Instead of speed distribution of each stream, the cumulativepercentages of speed of each type of vehicle have also been calculated in order to have anaverage speed of each type of vehicle from each stream. Previous data have shown (Table 4-10 and Table 4-11) that the numbers of motorcycles (MC), cars (LV) and truck 2–axles(MHV2) occurred more often than others and this is the case at all intersections. Therefore, wefound a smooth line of cumulative percentage of speed for those vehicles (see Figure 4-33).Motorcycles have demonstrated a higher flow and average speed than others, but cars wouldhave a significant impact on intersections under mixed conditions which relate to theopportunity of maneuvering others due to its projected rectangular area.

30,027,525,022,520,017,515,012,510,07,55,02,50,0

Speed (km/h)

70

60

50

40

30

20

10

0

Freq

uenc

y

Mean =16,619�Std. Dev. =1,8798�

N =480

Flow direction B - C

35,032,530,027,525,022,520,017,515,012,510,07,55,02,50,0

Speed (km/h)

200

190

180

170

160

150

140

130

120

110

100

90

80

70

60

50

40

30

20

10

0

Freq

uenc

y

Mean =24,852�Std. Dev. =2,6209�

N =1.300

Flow direction A - C

35,032,530,027,525,022,520,017,515,012,510,07,55,02,50,0

Speed (km/h)

100

90

80

70

60

50

40

30

20

10

0

Freq

uenc

y

Mean =26,171�Std. Dev. =2,8664�

N =748

Flow direction A - B

30,027,525,022,520,017,515,012,510,07,55,02,50,0

Speed (km/h)

80

70

60

50

40

30

20

10

0

Freq

uenc

y

Mean =20,836�Std. Dev. =3,5422�

N =571

Flow direction B - A


85

Figure 4-33. Typical Cumulative Distribution of Speed of Each Type of Vehicle of Stream C – A (1)

4.8 Intersection Occupancy (percent of intersection area)

Road occupancy is expressed as total area covered by vehicles in relation to the road area. Aspreviously mentioned every type of vehicle has its own vehicular area or its dimensions whichvary widely. Intersection occupancy is defined as the physical area of the vehicles relative tothe intersection area. This measurement is adopted since the vehicles of heterogeneous trafficmix have wide variations in their dimensions. Intersection occupancy was constructed fromreference lines that have been marked at the intersections and from that we can simply findthe total area of conflict (Figure 4-34).

MARWAH & SINGH (2000) have observed the level of service (LOS) of urbanheterogeneous traffic flow condition at the Kanpur roads, India. They used a method of roadconcentration (number of vehicles per km) which can also be realistically expressed in termsof vehicle road occupancy (total projected rectangular area of vehicles per total area of roadsections). Road concentration is observed at an interval of every 100 seconds. Results showthat concentrations increase almost on a linear basis with the flow level. We also observedthat road occupancy increases at a certain rate up to 1800 veh/h and beyond this flow, the rateof increase is higher. The analysis and results have demonstrated that speed and concentrationare affected by flow level. It was also observed that mean journey speed of all vehicles variesalmost linearly with concentration.

56,7

49,3

45,0

41,4

38,4

35,8

33,5

31,8

30,0

28,4

26,9

25,6

24,4

23,3

22,3

21,4

20,6

19,8

19,1

17,5

16,6

15,4

13,58,3

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge

Speed distribution of flow direction C - A

RickshawBicycleMotorcycleCarMinibusTruck 2 axlesTruck 3 axles

Type of vehicle


86

Figure 4-34. Description of Intersection Occupancy Measurement

For the estimation of the intersection occupancy, the intersections’ field area has beenreconstructed as shown in Figure 4-34 which line references at each leg of the intersection andedges of the road were used to determine the total area of conflict. The total area of conflictcould easily be determined by using the reference lines (at the leg of intersections and theedge of a road), results from measurement can be seen in Table 4-22. The reference lines weredrawn on the field by a special paint and color (white) and these lines have to be made asbright as possible so that they can be seen from the camcorder, clearly. By using data of staticdimensions of each type of vehicle (length and width) mentioned in Table 4-18, vehicles’occupancy in certain areas of intersections can simply be measured (Table 4-23).Intersection–1 found to have a larger area of conflict than others due to its geometric design.This would be an opportunity for vehicles to maintain their speed during their travellingacross the conflict area. Appendix C shows that the speed of vehicles at intersection–1 ishigher than of others.

C-A

C-B

A-C

A-B

B-A

B-C

Stream

Travel stream

Reference line

5

4

6

2

1

3

Area of conflict


87

Intersection Investigated Intersection – Conflict Area[m2]

1 434.432 361.673 121.004 85.005 163.356 167.927 215.208 66.909 127.3710 215.49

Table 4-22. Conflict Area of Each Intersection

Type of Vehicles Vehicles Occupancy[m2]

Truck 3 axle 17.28Truck 2 axle 17.28Minibuses 10.26Car 6.48Motorcycle 1.28Bicycle 1.05Becak (Rickshaw) 2.25Tricycles 1.68Pushcart (2-wheels) 1.68

Table 4-23. Each of Vehicles Occupancy

The intersections’ occupancy was observed within 20–second intervals to have an averagevalue of 1–minute intervals in which a certain number of vehicles was monitored while theypass over the intersection in a certain time. This observation has been done by monitoring thevehicle’s movements at the monitor and time–code machine. Then we could count the numberof vehicles at the conflict area within every 20–seconds. The results (e.g. intersection–1) canbe seen in Figure 4-35 which have demonstrated intersection occupancy (%) within a range of1.0% – 8.0% (maximum). From the results of flow and intersection occupancy relationship,this study would try to look further on the impact of traffic flow and maximum flow(capacity) related to the occupied percentage (%) area of conflict. Typical intersectionoccupancy for all intersections is shown in the figures in Appendix A. We found only that theintersections’ occupancy would likely to be in the range of 8% to 17% where intersection–9and intersection–10 performed the smaller percentage of 8%.


88

Figure 4-35. Typical Intersection Occupancy within 1–minute intervals

4.9 Conclusions

Ten three–leg unsignalized intersections have been investigated and analyzed. They havevarious widths of legs (geometric design). Flows and composition (type of vehicles) wouldcontributed to various traffic speeds. Types of vehicles were classified into nine (9) categoriesdiffering in static and dynamic characteristics. Motorcycles have the largest percentage of70% – 88% and higher average mean speed than others. Each vehicle movement from eachstream was observed by using video. Furthermore, speed and flow of each type of vehiclefrom each stream and total vehicles’ occupancy were counted.

A large number of vehicle types which differ in characteristics give impacts in trafficperformance while they mixed. Therefore, this study has determined values for passenger carunits (PCUs) based on performance of speed and projected rectangular area of vehicles.Results showed that each vehicle performed at different speeds. Also the same type of vehiclehas also performed at different speed between streams’ flow. The values have been used forfurther analysis of flows’ stream in passenger car units (PCUs).

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Intersection occupancy (%)


89

Speed and flow were counted in every 1–minute and 5–minute interval time while intersectionoccupancy was counted in every 20–second interval. By analysis of one hour from two hourfield observation, numbers of vehicles (flows) in 1–minute and 5–minute intervals haveshown some fluctuation. It was not possible to observe the real of flow and at theintersections. But, from the total ten hours of investigation from ten unsignalized intersectionsthe real condition of mixed traffic streams could be represented.

5 New Approach of Capacity Calculation Based on Conflicting Streams

90

5 New Approach of Capacity Calculation Based on ConflictingStreams

5.1 Introduction

The empirical study of traffic operation at unsignalized intersections as it is described in theprevious paragraphs showed that the traditional methodologies for capacity analysis, as theyhave been established for developed countries, are not applicable for countries like Indonesia.Current behavior such as of very short gaps acceptance (less than 2 seconds), a large numberof non–motorized vehicles which have many different speeds, no lane discipline where manyconflicts must be expected. Current investigations found that within a flow of 4900 veh/h –7200 veh/h, the number of vehicle stops is only 0.4% – 0.5%. Therefore, in such a case ofmixed traffic flow at unsignalized intersections the capacity is difficult to measure, when theflow is not saturated. Two methods (gap acceptance and empirical approach) of capacity areused in the saturated traffic. They are difficult to apply in such a mixed traffic.

Evidence shows that a relationship between speed and flow was found in every case of mixedtraffic at a certain segment of road. This relationship might be more complex if vehicle of alltypes (MHV, LV, MC, etc.) and the percentage of slow–moving vehicles were taken intoaccount. This idea of a relationship between speed and flow was used for further experiments.Investigated data did not only include speed and flow of each stream but also speed and flowof each type of vehicle from each stream. However, for the first step, each stream should begrouped based on its conflict pattern.

5.2 Conflicting Stream Description and Conflict Group

General scheme of vehicle streams at three–leg unsignalized intersections were defined toconstruct the conflict streams. Many researchers have taken the scheme for capacity analysispurposes, e.g. KIMBER & COOMBE (1980) for three–leg intersections, however, they havenot defined the conflicts at any point instead of group streams: left–turning (CB-C, CB-A) right–turning minor road stream (CC-B, CC-A) see Figure 3-5. Effects of geometric design of anintersection were considered. Flows of different types of vehicles were also considered butonly for light and heavy vehicles (LV and HV). By this approach, one must have a saturatedflow – the value the capacity would take if all road flows were zero and rule of priority wasapplied. However, under mixed traffic flow, intersections were difficult to be saturated sincetraffic keeps moving under all circumstances.

Recent analyses are based on interactions between streams which include parameters ofjourney speed, flow and road occupancy. Due to these circumstances, each of the parametersof each stream has to be analyzed related to other streams included. The scheme consists ofsix (6) streams (C – A, C – B, B – C, B – A, A – C, A – B) and six (6) conflict points (1, 2,


91

3, 4, 5, 6). Furthermore, it is proposed to have six (6) groups of conflicts (I, II, III, IV, V, VI)which include all streams’ conflicts and each group has its own subject stream (see also Table5-1). Because this study does not use any of the priority rules, six subject streams have to bedefined for a further step of analysis. Each stream (C – A, C – B, B – C, B – A, A – C,A – B) remains the subject stream of its conflict group. Each of them should be consideredfurther in order to find their maximum flow. In general, the conflict groups were defined asthe subject streams which met conflict movement with other streams, e.g. subject streamC – A has only met one conflict movement with stream B – A, but subject stream B – Awould meet more streams (C – A, C – B and A – C).

Figure 5-1. Scheme of Conflict of Traffic Streams

Group of Conflict Subject Stream Conflict Point Streams Involved

I C – A 1 C – A, B – AII C – B 2,4,5 C – B, B – A, A – C, A – BIII B – C 3 B – C, A – CIV B – A 1,4,6 B – A, A – C, C – B, C – AV A – C 3,5,6 A – C, C – B, B – A, B – CVI A – B 2 A – B, C – B

Table 5-1. Interaction of Traffic Streams of Each Conflict Group

Further analysis of speed, flow and road occupancy of each stream (each type of vehicle)always relies on this description. Interactions regarding parameters of vehicles wereperformed at further steps. Investigations of conflict groups were conducted in terms of flowand speed in each flow stream and its portion based on total flow of its group was counted.Table 5-2 to 5-7 show examples of flow portions of each group (intersection–1) in which it isnecessary to have details of the portion of vehicles/flows, because smaller portion offlows (< 1.0 %) could not adequately be included in the analysis which could produce some

1

2

3

4

56

C-A

C-B

A-C

A-B

B-A

B-C

Area of conflict

Conflict pointStream

A

B

C


92

error in regression analysis by using the Statistical Package for Social Sciences Software(SPSS version 14.0, 2006). The portion of vehicles is counted from the total number ofvehicles/flow in each conflict group. The software is required for further calculation becausethe study dealt with a large number of vehicles (e.g. 7240 vehicles per hour), six subjectstreams and various types of vehicles (9 types) with many types of calculations/operations(e.g. speed, flow, occupancy). Details of traffic flow performance of all intersections areshown in Appendix B. The tables show that most of the cases (intersections observed) havefound that only motorcycles and cars could be taken into account because they always have alarge number of vehicles.

StreamsC – A B – AType of Vehicle

Flow [veh/h] % Flow

[veh/h] %

Truck 3 axle 12 0.7 N.A N.ATruck 2 axle 51 2.9 16 0.9Minibuses 2 0.1 N.A N.ACar 199 11.2 140 7.9Motorcycle 927 52.2 395 22.2Bicycle 8 0.5 14 0.8Becak (Rickshaw) 7 0.4 6 0.3Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

1206 571Table 5-2. Vehicles Composition of Conflict Group–I

StreamsC – B B – A A – C A – BType of

Vehicle Flow[veh/h] % Flow

[veh/h] % Flow[veh/h] % Flow

[veh/h] %

Truck3 axle N.A N.A N.A N.A 8 0,3 N.A N.A

Truck2 axle 24 0.8 16 0.5 69 2.3 21 0.7

Minibuses N.A N.A N.A N.A 5 0.2 3 0.1Car 51 1.7 140 4.8 211 7.2 163 5.5Motorcycle 242 8.2 395 13.4 987 33.6 546 18.6Bicycle 3 0.1 14 0.5 15 0.5 11 0.4Becak(Rickshaw) 1 0.0 6 0.2 5 0.2 3 0.1

Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A 1 0.0

480 571 1300 748Table 5-3. Vehicles Composition of Conflict Group–II


93

StreamsB – C A – CType of Vehicle

Flow [veh/h] % Flow

[veh/h] %

Truck 3 axle N.A N.A 8 0.4Truck 2 axle 31 1.7 69 3.9Minibuses N.A N.A 5 0.3Car 55 3.1 211 11.9Motorcycle 385 21.6 987 55.4Bicycle 7 0.4 15 0.8Becak (Rickshaw) 2 0.1 5 0.3Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

480 1300Table 5-4. Vehicles Composition of Conflict Group–III

Based on a field investigation, vehicles’ composition was different between one intersectionand another. This condition slightly depends on the intersections’ environment depending onthe fact whether it is a commercial area, a residential area or a restricted access. Intersection–2and intersection–3 are located in a commercial area or traditional market. Therefore, theintersections performed high portion of trucks and non–motorized vehicles, because most ofthe people tend to use (traditional) public transport, e.g. rickshaw (becak) and private vehiclessuch as bicycles and motorcycles in order to reach the place. The drivers considered to usesuch vehicles due to the short distance, an easiest way to park the vehicles and the cheapestprice to transport their belongings, e.g. rickshaw/becak.

StreamsC – A C – B B – A A – CType of



[veh/h] %

Truck3 axle 12 0.4 N.A N.A N.A N.A 8 0.2

Truck2 axle 51 1.5 24 0.7 16 0.5 69 2.0

Minibuses 2 0.1 N.A N.A N.A N.A 5 0.1Car 199 5.9 51 1.5 140 4.1 211 6.2Motorcycle 927 27.3 242 7.1 395 11.6 987 29.0Bicycle 8 0.2 3 0.1 14 0.4 15 0.4Becak(Rickshaw) 7 0.2 1 0.0 6 0.2 5 0.1

Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

1206 321 571 1300Table 5-5. Vehicles Composition of Conflict Group–IV


94

However, in residential areas and restricted areas, the intersections seem to have higherportion of motorized (e.g. motorcycle and cars) and very small percentage of non–motorizedvehicles. Intersection–8 and intersection–10 are located in a residential area and the fieldinvestigation, showed that there were 128 and 73 non–motorized vehicles with less than 5%from the total number of vehicles at intersections.

StreamsC – B B – C B – A A – CType of



[veh/h] %

Truck3 axle N.A N.A N.A N.A N.A N.A 8 0.3

Truck2 axle 24 0.9 31 1.2 16 0.6 69 2.6

Minibuses N.A N.A N.A N.A N.A N.A 5 0.2Car 51 1.9 55 2.1 140 5.2 211 7.9Motorcycle 242 9.1 385 14.4 395 14.8 987 36.9Bicycle 3 0.1 7 0.3 14 0.5 15 0.6Becak(Rickshaw) 1 0.0 2 0.1 6 0.2 5 0.2

Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

1206 321 571 1300Table 5-6. Vehicles Composition of Conflict Group–V

StreamsC – B A – BType of Vehicle

Flow [veh/h] % Flow

[veh/h] %

Truck 3 axle N.A N.A N.A N.ATruck 2 axle 24 2.2 21 2.0Minibuses N.A N.A 3 0.3Car 51 4.8 163 15.2Motorcycle 242 22.6 546 51.1Bicycle 3 0.3 11 1.0Becak (Rickshaw) 1 0.1 3 0.3Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A 1 0.1

321 748Table 5-7. Vehicles Composition of Conflict Group–VI

Mean speeds have been determined for each stream at its conflict group. This is counted notonly based on each type of vehicle but also on the total average speed in a stream, becausefurther analysis would be conducted in two different approaches. The first is the relationshipbetween speed and flow of streams and the second is the relationship between speed and flowof each type of vehicle in a stream, see Table 5-8 and details in Appendix C.


95


Speed [km/h]Truck 3 axle 27.0 N.A N.A N.A 21.4 N.ATruck 2 axle 26.5 11.2 10.9 16.6 22.1 18.7Minibuses 27.1 N.A N.A N.A 17.7 21.4Car 27.6 11.6 11.9 17.6 21.9 21.4Motorcycle 31.8 14.9 17.9 22.6 25.9 28.2Bicycle 16.4 9.3 12.1 12.8 17.3 17.6Becak (Rickshaw) 13.2 5.0 7.3 8.4 13.4 13.8Tricycles N.A N.A N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A N.A 5.4

Mean Speed [km/h]30.6 14.0 16.6 20.8 24.9 26.2

Table 5-8. Typical Mean Speed Performance of Each Stream (intersection–1)

Figure 5-2. Traffic Performance of Conflict Figure 5-3. Speed Performance of Conflict Group–I Group–I

Figure 5-4. Traffic Performance of Conflict Figure 5-5. Speed Performance of Conflict Group–II Group–II

5957555351494745434139373533312927252321191715131197531

1-minute interval

40

35

30

25

20

15

10

5

0

Flow

(veh

/1-m

inut

e)

B - AC - A

Direction of flowGroup conflict I

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flowGroup conflict I

5957555351494745434139373533312927252321191715131197531

1-minute interval

50

45

40

35

30

25

20

15

10

5

0

Flow

(veh

/1-m

inut

e)

A - BA - CB - AC - B

Direction of flowGroup conflict II

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

Group conflict II


Direction of flow


96

Figure 5-6. Traffic Performance of Conflict Figure 5-7. Speed Performance of Conflict Group–III Group–III

Figure 5-8. Traffic Performance of Conflict Figure 5-9. Speed Performance of Conflict Group–IV Group–IV

Figure 5-10. Traffic Performance of Conflict Figure 5-11. Speed Performance of Conflict Group–V Group–V

5957555351494745434139373533312927252321191715131197531

1-minute interval

50

45

40

35

30

25

20

15

10

5

0

Flow

(veh

/1-m

inut

e)

A - CB - C

Direction of flowGroup conflict III

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flowGroup conflict III

5957555351494745434139373533312927252321191715131197531

1-minute interval

50

45

40

35

30

25

20

15

10

5

0

Flow

(veh

/1-m

inut

e)

Group conflict IV

A - CB - AC - BC - A

Direction of flow

5957555351494745434139373533312927252321191715131197531

1-minute interval

50,0

47,5

45,0

42,5

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

-2,5

Mea

n Sp

eed

(km

/h)

Group conflict IV


Direction of flow

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Flow

(veh

/1-m

inut

e)

Group conflict V

A - CB - AB - CC - B

Direction of flow

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

Group conflict V


Direction of flow


97

Figure 5-12. Traffic Performance of Conflict Figure 5-13. Speed Performance of Conflict Group–VI Group–VI

The table shows an example of data calculated from intersection–1 and performs the meanspeeds of each vehicle and average speeds of stream. The performance of speed was alsodescribed in the following figures in detail (Figure 5-2 to 5-13). We can see from the figuresthat the major flows (A – C and C – A) would have higher average flow and speed in most ofthe cases (see also Appendix A).

It is difficult to compare data performance for intersections because each of them has severaldifferences, e.g. portion of streams and geometric designs. Figure 5-14 to 5-17 show adifferent portion of each stream flow and mean speed from intersection–2 and intersection–3.Both have different geometric designs where intersection–2 has the following widths ofapproaches: A=10.6 m, B=19.5 m, C=10.6 m and intersection–3 : A=9.6 m, B=6.5 m, C=8.0m. This also happens at intersection–4 and intersection–7 which both have almost the sameflow but perform different speeds and different widths of approaches.

Figure 5-14. Portion of Each Stream Flow (2) Figure 5-15. Mean Speed Characteristics with Total Number 4928 veh/h

5957555351494745434139373533312927252321191715131197531

1-minute interval

30

25

20

15

10

5

0

Flow

(veh

/1-m

inut

e)

Group conflict VI

A - BC - B

Direction of flow

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

Group conflict VI

A - BC - B

Direction of flow


Direction of flow

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

569

1.238

549

795

681

1.097

5957555351494745434139373533312927252321191715131197531

1-minute interval

50,0

47,5

45,0

42,5

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow


98

Figure 5-16. Portion of Each Stream Flow (3) Figure 5-17. Mean Speed Characteristics with Total Number 4902 veh/h

Therefore, the study also found difficulties to analyze the general relationship between flowand speed of each stream which can represent the relationship for all intersections, becausenot each intersection has been designed very well due to some circumstances, e.g. lack ofbudget and data resources (data is not accurate).

5.3 Speed and Flow Performance of Conflict Groups

In the preliminary investigation, the speed of cars from each stream was measured in relationto the interactions with other streams (flows) in the conflict group. The bar chart/graph of aflow and the mean speed of each group of conflict showed that the proportion of speed hasfollowed the portion of flow (see also Appendix A).

Aggregated short–based 1–minute and 5–minutes speed and flow data were used to testspeed – flow and intersection occupancy – flow models for flat/normal conditions. Eachsample in this database represents the average speed and flow value for all observed1–minute and 5–minute intervals. The predetermined flows fall in range 438.4 pcu/h – 2209.4pcu/h for all sites. The database covered 10 sites with a total analysis of 10 hours flow, speedand intersection occupancy observations. The impacts of vehicle types from each streamwithin a group of conflict at each intersection were analyzed with multiple regressions. Thisstudy has not analyzed the impact of site conditions (carriageway width, side friction, landuse, road function class, sight distance class). Multiple regressions have been made based onspeed and flow performance.

Previously, it is explained that in such a road section, speed of light vehicles could easily bemeasured from interactions between vehicles in their own stream. Further steps might be donein accordance to the non–motorized vehicles and other factors related to ”side frictions” and


Direction of flow

2.600

2.400

2.200

2.000

1.800

1.600

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

118

1.141

115

446

701

2.381

Flow of each stream

5957555351494745434139373533312927252321191715131197531

1-minute interval

60,0

57,5

55,0

52,5

50,0

47,5

45,0

42,5

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow


99

other conflict streams. For further analysis of relationships and interactions between speedand flow streams it has to be assumed that the function is linear. This has been explained inthe previous chapter and the following Equation 5-1.

This linear function did not consider non–motorized vehicles due to the fact that the numberof them was less than 1%. The idea is to keep this linear function applied in intersectionanalysis by also considering an idea from the method of KIMBER et al. (1980) andRAMANAYYA (1988). The assumptions were made that the regression analysis of the speedand flow relationship is linear where only light vehicle (LV), heavy vehicle (HV), motorcycle(MC) and unmotorized (UM) were considered. Therefore, the speed of light vehicles of eachstream could be determined as

UMiUMiMCiMCiHViHViLViLViLVi QaQaQaQaConst.V ⋅−−⋅−⋅−⋅−= ....

whereVLVi = Average speed of light vehicle (LV) of stream i [km/h]Const. = Constant value representing free–speed of

light vehicle stream i [-]aji = Speed reduction effect of vehicle j

(j = LV, HV, MC, UM) at stream i [-]Qji = Flow of vehicle j at stream i [pcu/h]

Speeds of each stream have been observed based on their flow of group conflict. Speed andflow line regressions were then made for each group of conflict for all intersections. A linearspeed and flow model of each stream at a group conflict obtained relationship with R2 < 0.5,see Figure 5-18 to 5-23 in most of the cases. The figures show one of the observedintersections while others still have the same performance at the same range of R2 (0.1 –0.45).

Figure 5-18. Speed Performance of Each Figure 5-19. Speed Performance of Each Stream at Conflict Group–I Stream at Conflict Group–II

(5-1)

50,045,040,035,030,025,020,015,010,05,00,0

Flow (pcu/1-minute interval)

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Group of conflict I

B - AC - A

Direction of flow

50,045,040,035,030,025,020,015,010,05,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Group of conflict II


Direction of flow


100

Figure 5-20. Speed Performance of Each Figure 5-21. Speed Performance of Each Stream at Conflict Group–III Stream at Conflict Group–IV

Figure 5-22. Speed Performance of Each Figure 5-23. Speed Performance of Each Stream at Conflict Group–V Stream at Conflict Group–VI

5.4 Speed of Each Stream and the Total Flow of Conflict Group

Linear models for speed (subject stream, e.g. C – A) and flow which was defined as the totalnumber of vehicles of streams included in conflict groups (e.g. C – A and B – A) were alsoobserved. This part of observation is necessary to find further detailed relationships betweenspeed and flow divided by group of conflict. A linear model for the relationship is found tohave R2 in a range of 0.2 – 0.45 with no apparent knee in the relationship, see Figure 5-24 to5-29 . Since observations were only performed for one (1) hour, data are concentrated on asmall range of speed and flow, we used a linear model. Similar linear relationships wereobtained for selected intersections.

Various levels of correlation were found for the relation between subject stream and groupconflict flow. These levels are indicated at values of R2. The level could be smaller if thestream faced more conflicts and lower number of vehicles than other streams, e.g. stream B

50,045,040,035,030,025,020,015,010,05,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Group of conflict III

A - CB - C

Direction of flow

50,045,040,035,030,025,020,015,010,05,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Group of conflict IV


Direction of flow

50,045,040,035,030,025,020,015,010,05,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Group of conflict V


Direction of flow

50,045,040,035,030,025,020,015,010,05,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Group of conflict VI

A - BC - B

Direction of flow


101

– A and C – B. Drivers tend to choose the easiest way to pass through the intersection,especially when they find more space (small number of vehicles occupied the intersectionarea) to make such a maneuver which could lead to unusual travel paths for their movement.This might contribute to a higher speed then expected. This would not be suitable to representthe speed of the specific stream. Therefore, it is necessary to have a consistency in travelpaths for each stream to have appropriate speed and vehicles’ travel path were neglected.Based on the field investigation, it was found that some drivers did not use their normal travelpath or they used shorter distances in order to complete their travelling cross the intersectionswhich results in a higher speed than the average speed at the same travel path/distance. Thiscase mostly occurred at the turning movement of streams, e.g. stream C – B and B – A. Inthis case, it is required to separate the data from vehicles with unusual travel paths whichmeans this data should be neglected. Every streams’ flow which has been observed in thisstudy is used for further analysis. Further relationships between the speed of light vehicles ofstreams and flow of streams’ conflict group are constructed as :

conflict)of(groupQaconflict)of(groupQaconflict)of(groupQaconflict)of(groupQaConst.V

UMUMMCMC

HVHVLVLVLVi

⋅−−⋅−⋅−⋅−=

....

where

VLVi = Average speed of light vehicle (LV) of stream i [km/h]Const. = Constant value representing free–speed of light vehicle stream i [-]aj = Speed reduction effect of vehicle j (j = LV, HV, MC, UM) (j = LV, HV, MC, UM) [-]Qj = Flow of vehicle j [pcu/h]

Figure 5-24. Speed Performance of Stream Figure 5-25. Speed Performance of Stream C – A (1) C – B (2)

(5-2)

160,0150,0140,0130,0120,0110,0100,090,080,070,060,050,040,030,020,010,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Flow direction C - A

130,0120,0110,0100,090,080,070,060,050,040,030,020,010,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Flow direction C - B


102

Figure 5-26. Speed Performance of Stream Figure 5-27. Speed Performance of Stream B – C (3) B – A (4)

Figure 5-28. Speed Performance of Stream Figure 5-29. Speed Performance of Stream A – C (5) A – B (6)

5.5 Speed and Flow Relationship for Each Type of Vehicle Stream

Due to a large number of different parameter values with regard to various types of vehiclesin this study, e.g. speed and flow, it is proposed to construct relationships between parametersincluding each vehicle’s performances (LT, MHV, LV, MC, UM, see also Table 4-7) fromeach stream as follows

Conflict group – I,

BAUMiBAUMiUMi-CAUMi-CABAMHViBAMHVi

MHVi-CAMHVi-CALV-BABALVLV-CALV-CALV-CA

QKQK....QKQKQKQKAV

−−−−

−

⋅−⋅−−⋅−⋅−⋅−⋅−=

conflict group – II,

ABUMiABUMiACUMiACUMiBAUMiBAUMiUMi-CBUMi-CB

ABMHViABMHViACMHViACMHViBAMHViBAMHViMHVi-CBMHVi-CB

LV-ABABLVLV-ACACLVLV-BABALVLV-CBLV-CBLV-CB

QKQKQKQK....QKQKQKQK

QKQKQKQKAV

−−−−−−

−−−−−−

−−−

⋅−⋅−⋅−⋅−−⋅−⋅−⋅−⋅

−⋅−⋅−⋅−⋅−=

(5-3)

(5-4)

100,090,080,070,060,050,040,030,020,010,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Flow direction B - C

100,090,080,070,060,050,040,030,020,010,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Flow direction B - A

100,090,080,070,060,050,040,030,020,010,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Flow direction A - C

100,090,080,070,060,050,040,030,020,010,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)Flow direction A - B


103

conflict group – III,

ACUMiACUMiUMi-BCUMi-BCACMHViACMHVi

MHVi-BCMHVi-BCLV-ACACLVLV-BCLV-BCLV-BC

QKQK....QKQKQKQKAV

−−−−

−

⋅−⋅−−⋅−⋅−⋅−⋅−=

conflict group – IV,

CAUMiCAUMiCBUMiCBUMiACUMiACUMiUMi-BAUMi-BA

CAMHViCAMHViCBMHViCBMHViACMHViACMHViMHVi-BAMHVi-BA

LV-CACALVLV-CBCBLVLV-ACACLVLV-BALV-BALV-BA


QKQKQKQKAV

−−−−−−

−−−−−−

−−−

⋅−⋅−⋅−⋅−−⋅−⋅−⋅−⋅

−⋅−⋅−⋅−⋅−=

conflict group – V,

BCUMiBCUMiBAUMiBAUMiCBUMiCBUMiUMi-ACUMi-AC

BCMHViBCMHViBAMHViBAMHViCBMHViCBMHViMHVi-ACMHVi-AC

LV-BCBCLVLV-BABALVLV-CBCBLVLV-ACLV-ACLV-AC


QKQKQKQKAV

−−−−−−

−−−−−−

−−−

⋅−⋅−⋅−⋅−−⋅−⋅−⋅−⋅

−⋅−⋅−⋅−⋅−=

conflict group – VI

CBUMiCBUMiUMi-ABUMi-ABCBMHViCBMHVi

MHVi-ABMHVi-ABLV-CBCBLVLV-ABLV-ABLV-AB

QKQK....QKQKQKQKAV

−−−−

−

⋅−⋅−−⋅−⋅−⋅−⋅−=

where

VLV-CA = Average speed of light vehicle (LV) stream C – A [km/h]A = Constant representing free–flow speed of light vehicle [km/h]KLV-CA = Speed reduction effect caused by light vehicle (LV) stream C – A [-]QLV-CA = Traffic flow for light vehicle (LV) stream C – A [pcu/h]KMHV-CA = Speed reduction effect caused by medium heavy vehicle (MHV) stream C – A [-]QMHV-CA = Traffic flow for medium heavy vehicle (MHV) stream C – A [pcu/h]

It is expected to have a certain number of each type of vehicle for being analyzed with theformulas mentioned above. The vehicles consist of nine (9) categories. However, thosevehicle types represented only with a portion lower than 1.0% were not be taken into accountbecause they were considered as elements of side friction rather than traffic flow (BANG etal., 1995). This mostly occurred for minibuses (LT) and unmotorized vehicles (UM) and itcould produce some error in developing a regression model. Table 5-9 and 5-10 show linearmodels with more than two parameters within 1–minute and 5–minute interval observations.The linear model gave a suitable relationship which partly has a very close relationship(R2 > 0.900).

(5-5)

(5-6)

(5-7)

(5-8)


104

A suitable correlation between the speed of light vehicles/cars (LV) of each stream and flowof each type of vehicle of conflict group has been found at almost all of the intersections.Details can also be seen in Table 5-16. The relationship was constructed between the speed oflight vehicles of a subject stream and the flow of each type of vehicle included in the conflictgroup, e.g. speed of light vehicles of subject stream C – A, VLV-CA is a function of flow ofevery type of vehicle in a conflict group (QMHVi-CA, QMHVi-BA, QLV-CA, QLV-BA, QMC-CA, QMC-BA,QUMi-CA, QUMi-BA, ....). In most of the cases, intersections could only perform the relationshipwith the flow of medium heavy vehicles (MHV), light vehicles (LV) and motorcycles (MC)due to their number (flow) and portion which are more than 1.0%.

From the tables (Table 5-9 and Table 5-10) we can see that the relationship is even better (R2)in 5–minute interval observations than in a 1–minute observations. Considering this level ofcorrelation between parameters, this was not just the number of vehicles’ stream as importantvalue, but also the number of each type of vehicle within the streams.

Speed – Flow Relationship

Group of Conflict Form R2

IBAMCCAMCBALV

CALVCAMHVCALV

QQQQQV

−−−

−−−

++−−−=

532.0227.1055.1361.0371.0917.28 2 0.303

II

ABMC

ACMCBAMCCBMC

ABLVACLVBALV

CBLVACMHVCBLV

QQQQ

QQQQQV

−

−−−

−−−

−−−

−−+−−+

+++=

915.0298.1496.0836.1

361.0095.0436.0334.0494.0777.15 2

0.392

IIIACMC

BCMCACLVBCLV

ACMHVBCMHVBCLV

QQQQ

QQV

−

−−−

−−−

−−−

−++=

80.0155.202.1947.0

023.0391.0670.21 22

0.445

IV

ACMCBAMC

CBMCCAMCACLV

BALVCBLVCALV

ACMHVCAMHVBALV

QQQQQ

QQQQQV

−−

−−−

−−−

−−−

++−+−

−−+−+=

952.1928.5336.5620.0737.0

189.0867.0605.1466.1125.0463.10 22

0.547

V

ACMC

ACLVACMHVBAMC

BALVBCMCBCLV

CBMCCBLVACLV

QQQQ

QQQQQV

−

−−−

−−−

−−−

−−+−

+++−+=

384.0062.0416.0968.3

058.0086.1216.3421.6945.3001.21

20.910

VIABMCABLV

CBMCCBLVABLV

QQQQV

−−

−−−

−+++=

321.2312.0054.0339.3465.18

0.901

Table 5-9. Speed – Flow Relationship Based on Each Type of Vehicle in 1–minute intervals


105

Speed – Flow Relationship

Group of Conflict Form R2

IBAMCCAMCBALV

CALVCAMHVCALV

QQQQQV

−−−

−−−

++−−−=

204.0498.0405.0061.002.0658.24 2 0.739

II

ABMC

ACMCBAMCCBMC

ABLVACLVBALV

CBLVACMHVCBLV

QQQQ

QQQQQV

−

−−−

−−−

−−−

−−−+

−−+−+=

759.1250.1021.4392.1

292.0632.0298.0238.0164.0062.79 2

0.941

IIIACMC

BCMCACLVBCLV

ACMVHBCMHVBCLV

QQQQ

QQV

−

−−−

−−−

++−

−++=

218.0221.1327.0114.0

060.0048.0387.5 22

0.537

IV

ACMCBAMC

CBMCCAMCACLV

BALVCBLVCALV

ACMHVCAMHVBALV

QQQQQ

QQQQQV

−−

−−−

−−−

−−−

++−−−

+++++−=

406.0337.4551.5149.0643.0

819.0811.0272.0022.0514.0439.5 22

0.995

V

ACMCBAMC

BCMCCBMCACLV

BALVBCLVCBLV

ACMHVBCMHVACLV

QQQQQ

QQQQQV

−−

−−−

−−−

−−−

+++−−

+++−+−=

443.0187.2892.0299.0265.0

178.011.0427.0108.0009.0338.2 22

0.989

VIABMC

CBMCABLVCBLV

ABMHVCBMHVABLV

QQQQ

QQV

−

−−−

−−−

−++

−+−=

111.0857.1181.0729.0

14.010.0173.16 22

0.214

Table 5-10. Speed – Flow Relationship Based on Each Type of Vehicle in 5–minute intervals

Therefore, 5–minute interval observations could result in a portion number of vehicles. Thusmore types of vehicles can be included in the analysis which contribute to the higher level ofcorrelation. However, the relationship between the speed of light vehicles and the flow ofeach type of vehicle in conflict groups, could not be used for further analysis of capacity butonly for speed and flow of streams.

5.6 Speed and Flow Relationship of Each Stream

Despite the speed and flow relationship between each type of vehicle from each stream, therelationship between the flow of each stream (QC-A, QC-B, QB-C, QB-A, QA-C, QA-B) of eachconflict group (I, II, III, IV, V, VI) and the speed of each stream (VC-A, VC-B, VB-C, VB-A, VA-C,VA-B) was also developed, because further capacity calculations would be based on eachstream performance of every conflict group. The development of this relationship wasrequired for further capacity analysis by the new approach, because the analysis would not bepossible if the flows of each type of vehicle were counted separately. Therefore, it is requiredto measure an average speed and the total flow of each stream of all intersections. Then the


106

relationship between speed and flows of conflict groups was constructed with a functionand scheme which is presented in Table 5-11. From the table we can see that the red lineremains the subject stream and another color represents the streams included in a conflictgroup.

The study considered that the subject streams, e.g. bC–A QC–A have also contributed to thereduction of its own speed, VC–A instead of other conflict streams (e.g. bB–AQB–A in group ofconflict–I). The study has also found that group of conflict–II, group of conflict–IV and groupof conflict–V have four streams included (C – B, B – A and A – C) while other groups haveonly two streams (C – A, B – C and A – B).

Group ofConflict

Function Scheme

I

II

III

1C-A

B-A

2

4

5

C-B

A-C

A-B

B-A

3

A-C

B-C

ABABACACACAC QbQbaV −−−−−− −−=

CACABABA

ABABBCBCBCBC

QbQbQbQbaV

−−−−

−−−−−−

−−−−=

CACACBCBCBCB QbQbaV −−−−−− −−=


107

IV

V

VI

Table 5-11. General Functions and Scheme of Speed – Flow Relationship at Conflict Group

Speed – Flow RelationshipGroup ofConflict Form R2 SE

I ABACAC QQV −−− −−= 148.032.05.34 0.215 2.2347

IIBA

CAABBCBC

QQQQV

−

−−−− −−−−=229.0

084.0268.0211.02.180.153 2.1735

III CACBCB QQV −−− −−= 091.0288.09.18 0.214 1.6706

IVCA

ABBCACAB

QQQQV

−

−−−− −++−=149.0

130.0084.0194.01.230.084 3.3620

VCA

ABCBBCCA

QQQQV

−

−−−− −−−−=311.0

265.044.0161.06.310.474 1.9045

VI BABCBA QQV −−− −−= 506.0014.04.29 0.230 2.5194Table 5-12. Speed – Flow Relationship Based on Each of Stream in 1–minute intervals

1

4

6

C-A

C-B

A-C

B-A

3

5

6

C-B

A-C

B-A

B-C

2

C-B

A-B

BCBCACAC

CACAABABABAB

QbQbQbQbaV

−−−−

−−−−−−

−−−−=

ABABCBCB

BCBCCACACACA

QbQbQbQbaV

−−−−

−−−−−−

−−−−=

BCBCBABABABA QbQbaV −−−−−− −−=


108

Speed – Flow RelationshipGroup ofConflict Form R2 SE

I ABACAC QQV −−− −−= 095.0014.04.33 0.082 1.0821

IIBA

CAABBCBC

QQQQV

−

−−−− ++−−=023.0

019.0045.0057.02.140.172 0.8870

III CACBCB QQV −−− −−= 074.0076.00.22 0.282 0.8672

IVCA

ABBCACAB

QQQQV

−

−−−− −−−−=058.0

157.0014.0045.08.290.531 0.6509

VCA

ABCBBCCA

QQQQV

−

−−−− −−−−=11.0

06.0053.0067.00.340.566 0.5962

VI BABCBA QQV −−− −+= 229.0117.03.31 0.438 1.6028Table 5-13. Speed – Flow Relationship Based on Each of Stream in 5–minute intervals

Table 5-12 and Table 5-13 show the relationship between average speed and flow in 1–minuteinterval and 5–minute interval observations which results in R2 within a range of 0.1 – 0.5. Itwas found that a weak relationship is given due to its number of conflicts and tendency ofdrivers to avoid lane discipline. In most of the cases, drivers tend to use unusual travel pathsin order to avoid the conflict with other streams and to complete their journey (departure fromconflict area) in a very short time.

Relationships of speed and flow in Table 5-12 and 5-13 have been determined forintersection–1. Also for the other intersections analysis has given similar relations with thesame level of R2–values. However, it is necessary to develop a model in general which issuitable for every intersection. The following equations were developed from combined dataof all intersections :

In a 1–minute interval,

54349.1;119.0

075.0191.0095.182 ==

−−= −−−

E

ABACAC

SR

QQV

62974.2;175.0

113.0234.0357.0069.0949.192 ==

+−−−= −−−−−

E

BACAABBCBC

SR

QQQQV

96911.0;343.0

033.0397.0799.132 ==

+−= −−−

E

CACBCB

SR

QQV

(5-9)

(5-10)

(5-11)


109

68863.1;304.0

337.0179.0443.0095.0064.162 ==

−−+−= −−−−−

E

CAABBCACAB

SR

QQQQV

36912.1;195.0

149.0205.0015.0060.0129.172 ==

−−−−= −−−−−

E

CAABCBBCCA

SR

QQQQV

38990.1;184.0

210.0207.0835.152 ==

−+= −−−

E

BABCBA

SR

QQV

In a 5–minute interval,

01472.1;179.0

017.0119.0638.212 ==

−−= −−−

E

ABACAC

SR

QQV

15856.1;374.0

150.0011.0105.0163.0023.272 ==

−−−−= −−−−−

E

BACAABBCBC

SR

QQQQV

29960.0;337.0

008.0054.0742.132 ==

−−= −−−

E

CACBCB

SR

QQV

62539.0;705.0

100.0082.0078.0106.0824.222 ==

−−+−= −−−−−

E

CAABBCACAB

SR

QQQQV

35753.0;395.0

075.0092.0009.0159.0380.192 ==

−−++= −−−−−

E

CAABCBBCCA

SR

QQQQV

69600.0;845.0

069.0093.0539.162 ==

−+= −−−

E

BABCBA

SR

QQV

5.7 Speed – Flow and Flow – Intersection Occupancy Relationship

Studies of level of service at any relationship between speed, flow, and road concentration atthe urban heterogeneous traffic flow have been carried out by MARWAH & SINGH (2000).The studies were conducted at roads with 500 meter length and 7 meter width. Simulationruns at flow levels of 600, 900, 1200, 1800, 2400, 3000, 3600, 4200 and 4800 vehicles perhour with every 100 second interval of observation were conducted for traffic concentration(number of vehicles per kilometer) and road occupancy (percent of road area occupied). Theresults show that the concentration increases almost linearly with increasing flow and roadoccupancy up to 1800 veh/h. Beyond this volume, the rate of increase was higher. Theanalysis has demonstrated that speed and concentration were affected by volume.

(5-12)

(5-13)

(5-14)

(5-15)

(5-16)

(5-17)

(5-18)

(5-19)

(5-20)


110

Observations also showed that the mean travel speed of all vehicles varies almost linearlywith concentration, however, for cars the speed variation was very high even at lowconcentration level.

The study above gave an indication of potential parameters to measure the quality of trafficflow at road sections and intersections based on speed, flow and intersection occupancy(percent area of intersection occupied by vehicles). Relationships for speed – intersectionoccupancy as well as flow – intersection occupancy for 1–minute, 5–minute, and 20–secondinterval observation were analyzed. The investigation starts with a regression analysis of theparameters: flow of each stream and intersection occupancy which is assumed to be a linearfunction,

),Q,Q,Q,Q,Q(QfIO A-BA.-CB-AB-CC-BC-A=

where

IO = Intersection Occupancy [%]QC-A = Flow of stream C – A [pcu/1–min]QC-B = Flow of stream C – B [pcu/1–min]QB-C = Flow of stream B – C [pcu/1–min]QB-A = Flow of stream B – A [pcu/1–min]QA-C = Flow of stream A – C [pcu/1–min]QA-B = Flow of stream A – B [pcu/1–min]

Occupancy was observed based on the sum of all vehicle’s area (length x width) projectedvertically relative to the area of conflict. This technique is adopted because the vehicles ofheterogeneous traffic mix have wide variations in their dimensions. Further analysis estimatedthe percentage of intersection–conflict area occupied by vehicles at the certain level of flowrate. Thus, it was expected to find out the degree of occupancy while capacity is reached. Thisis an important view since traffic behavior uses ”width acceptance concept” rather than ”laneconcept” in lane changing/overtaking because drivers, riders and pedestrians find it optimal toadvance by accepting lateral gaps (width) between preceding entities. Those models canultimately produce a good estimate of roadway capacity and assessment of operations andsafety of various facility designs (TIWARI, 2001). Figure 5-30 and Figure 5-31 show thelinear trend of following relationship in 1–minute interval observations where flow is definedas the total flow of all streams (in vehicles per hour and passenger car units per hour). Fromthe figures, we can see that 1–minute interval observation produced a suitable relationshipwith R2 = 0.104 (vehicles per hour) and R2 = 0.29 (passenger car units per hour). Thecorrelation coefficient were higher in 5–minute interval observation with R2 = 0.462 (vehiclesper hour) and R2 = 0.198 (passenger car units per hour). The speed performance of eachstream related to the percentage of intersection occupancy seems to be similar to the speedperformance in relation to the flow rate (see Figure 5-18 to 5-23). Observations in 5–minute

(5-21)


111

intervals can be seen in Figure 5-33 and Figure 5-34. We also found a typical speed andintersection occupancy relationship in 1–minute and 5–minute interval time (Figure 5-32 andFigure 5-35). It can be seen that each movement had its own speed level. Within eachmovement there is a tendency of speed decreasing with increasing intersection occupancy.

Figure 5-30. Flow – Intersection Occupancy Figure 5-31. Flow – Intersection Occupancy [veh/1–minute] [pcu/1–minute]

Figure 5-32. Speed – Intersection Occupancy of Each Stream in 1–minute intervals

15,014,013,012,011,010,09,08,07,06,05,04,03,02,01,00,0

Intersection Occupancy (%)

150

135

120

105

90

75

60

45

30

15

0

Flow

(veh

/1-m

inut

e in

terv

al)

Flow and Intersection Occupancy

15,014,013,012,011,010,09,08,07,06,05,04,03,02,01,00,0


100,0

90,0

80,0

70,0

60,0

50,0

40,0

30,0

20,0

10,0

0,0Fl

ow (p

cu/1

-min

ute

inte

rval

)


15,014,013,012,011,010,09,08,07,06,05,04,03,02,01,00,0


40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Spee

d (k

m/h

)

Speed of Each Stream and Intersection Occupancy


Direction of Flow


112

Figure 5-33. Flow – Intersection Occupancy Figure 5-34. Flow – Intersection Occupancy [veh/5–minute] [pcu/5–minute]

Figure 5-35. Speed – Intersection Occupancy of Each Stream in 5–minute intervals

In order to investigate the behavior of each traffic stream (six streams), more than twodimensional regression approaches have to be developed. Every stream contributes to anumber of vehicles occupying the intersections’ area and causing impact on flow and speed.

12,011,010,09,08,07,06,05,04,03,02,01,0


500

475

450

425

400

375

350

325

300

Flow

(veh

/5-m

inut

e in

terv

al)


12,011,010,09,08,07,06,05,04,03,02,01,00,0


300,0

275,0

250,0

225,0

200,0

175,0

150,0

125,0

100,0

Flow

(pcu

/5-m

inut

e in

terv

al)


15,014,013,012,011,010,09,08,07,06,05,04,03,02,01,00,0


40,0

35,0

30,0

25,0

20,0

15,0

10,0

5,0

0,0

Spee

d (k

m/h

)

Speed of Each Stream and Intersection Occupancy


Direction of Flow


113

The following Table 5-14 and Table 5-15 show an important correlation between intersectionoccupancy and the volume of each stream. These tables represent the functional relations foreach intersection. The results show that all intersections to have the same level of correlation(R2 and SE). However, we can see in Table 5-15 that observations in 5–minute intervalswould perform at a higher degree of correlation (R2 and SE) than 1–minute intervals.Therefore, it can be concluded that a suitable relationship between flow of each stream andintersection occupancy (conflict area occupied) can be well constructed. The idea ofdeveloping this relationship is to identify how large the conflict area occupation is at eachtraffic volume and how large it is at the maximum flow.

Intersection Form R2 SE

1BACA

ABCBBCAC

QQQQQQ

−−

−−−−

−+++++

009.0078.0023.0139.0105.0030.0049.0

0.173 1.442

2BACA

ABCBBCAC

QQQQQQ

−−

−−−−

++++++−

102.0100.0228.0063.0050.0121.0357.3

0.299 1.854

3BACA

ABCBBCAC

QQQQQQ

−−

−−−−

−+−+−−

386.0117.0282.0471.0082.0020.0393.4

0.167 3.140

4BACA

ABCBBCAC

QQQQQQ

−−

−−−−

++−++−

0001.0128.0054.0053.0542.0064.0289.3

0.101 3.211

5BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+++++−

161.0096.0461.0034.0209.0074.0445.0

0.252 2.469

6BACA

ABCBBCAC

QQQQQQ

−−

−−−−

−++−−+

0049052.0081.0245.0122.0152.0152.3

0.177 2.304

7BACA

ABCBBCAC

QQQQQQ

−−

−−−−

−+++−+

038.0038.0007.0101.0039.0041.0972.1

0.083 1.666

8BACA

ABCBBCAC

QQQQQQ

−−

−−−−

++++−+

147.0149.0374.0148.0216.0074.0608.0

0.288 2.209

9BACA

ABCBBCAC

QQQQQQ

−−

−−−−

−+++−−

080.0072.0232.0098.0075.0068.0618.1

0.189 1.635

10BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+−++−−

308.0077.0047.0065.0057.0117.0447.2

0.190 1.248

Table 5-14. Flow – Intersection Occupancy Relationship from 1–minute interval observation


114

Intersection Form R2 SE

1BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+++++−−

068.0056.0014.0058.0003.0031.0093.6

0.634 0.409

2BACA

ABCBBCAC

QQQQQQ

−−

−−−−

++−+−+−

016.0033.0016.0079.0024.0046.0095.6

0.243 1.881

3BACA

ABCBBCAC

QQQQQQ

−−

−−−−

++−+−−−

008.0062.0026.0069.0012.0003.0045.0

0.089 3.582

4BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+−+−+−

049.0019.0008.0134.0025.0019.0156.7

0.421 0.933

5BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+−+++−

036.0002.0053.0008.0018.0014.0724.6

0.156 0.855

6BACA

ABCBBCAC

QQQQQQ

−−

−−−−

−+−+++

087.0014.0006.0013.0108.0055.0852.4

0.595 0.748

7BACA

ABCBBCAC

QQQQQQ

−−

−−−−

−++++−−

231.0016.0082.0083.0052.0009.0511.0

0.742 0.473

8BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+++−++−

029.0039.0112.0031.0032.0063.0430.1

0.821 0.361

9BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+−+−−+−

076.0031.0099.0031.0045.0042.0308.2

0.722 0.418

10BACA

ABCBBCAC

QQQQQQ

−−

−−−−

+−+++−−

016.0006.0036.0041.0033.0061.0976.0

0.343 0.480

Table 5-15. Flow – Intersection Occupancy Relationship from 5–minute interval observations

The relationship between the traffic volume and intersections occupancy for all individualintersections has been presented above. The relation based on data from all intersections hasbeen obtained as :

For 1–minute interval,

12935.2;106.0

154.0042.0156.0085.0073.0136.0298.32 ==

++++++= −−−−−−

E

BACAABCBBCAC

SR

QQQQQQIO

and for 5–minutes interval,

25424.0;814.0

025.0039.0002.0038.0187.0114.0351.02 ==

−++++−= −−−−−−

E

BACAABCBBCAC

SR

QQQQQQIO

Measurement in 5–minute intervals produced unrealistic equations due to large various dataperformance of each stream (volume) of intersections. This was not used for further analyses.

(5-22)

(5-23)


115

The following Table 5-16 was made from all data measurements (intersections, conflict groupand interval time observations) to perform the correlation coefficients. All regressions in thischapter are summarized together. In most of the cases, we found the correlation are alwayshigher in 5–minute interval observations than in 1–minute interval observations.

Intersection ConflictGroup

TimeInterval[minute]

Speed – FlowConflict

[R2]

Speed – FlowConflict(vehicles

parameters)[R2]

IntersectionOccupancy –Flow Conflict

[R2]

1 0.215 0.303I 5 0.082 0.7391 0.153 0.392II 5 0.172 0.9411 0.214 0.445III 5 0.282 0.537 0.173

1 0.084 0.547IV 5 0.531 0.995 0.634

1 0.474 0.910V 5 0.566 0.9891 0.230 0.901

1

VI 5 0.438 0.2141 0.100 0.218I 5 0.339 0.5021 0.086 0.348II 5 0.245 0.4601 0.119 0.562III 5 0.003 0.571 0.299

1 0.726IV 5 0.387 0.860 0.243

1 0.232 0.509V 5 0.354 0.5331 0.030 0.365

2

VI 5 0.114 0.7571 0.340 0.234I 5 0.085 0.3711 0.418 0.164II 5 0.105 0.2411 0.149 0.916III 5 0.024 0.483 0.167

1 0.059 0.840IV 5 0.069 0.173 0.089

1 0.375 0.850V 5 0.262 0.9951 0.092 0.027

3

VI 5 0.062 0.5541 0.134 0.407I 5 0.025 0.8661 0.158 0.771II 5 0.733 0.8991 0.089 0.478III 5 0.256 0.629 0.101

1 0.247 0.709IV 5 0.301 0.971 0.421

1 0.288 0.630

4

V 5 0.548 0.970


116

1 0.209 0.292VI 5 0.702 0.5471 0.170 0.406I 5 0.376 0.3021 0.154 0.275II 5 0.431 0.8571 0.462 0.202III 5 0.286 0.807 0.252

1 0.124 0.691IV 5 0.802 0.784 0.156

1 0.251 0.255V 5 0.153 0.6761 0.057 0.140

5

VI 5 0.571 0.9991 0.181 0.483I 5 0.206 0.5311 0.149 0.721II 5 0.124 0.9731 0.185 0.101III 5 0.426 0.797 0.177

1 0.174 0.378IV 5 0.488 0.994 0.595

1 0.342 0.382V 5 0.723 0.8771 0.081 0.382

6

VI 5 0.065 0.2401 0.081 0.344I 5 0.057 0.6771 0.168 0.926II 5 0.455 0.9391 0.099 0.314III 5 0.074 0.676 0.083

1 0.209 0.345IV 5 0.478 0.989 0.742

1 0.241 0.682V 5 0.341 0.9751 0.103 0.528

7

VI 5 0.020 0.9181 0.082 0.596I 5 0.094 0.9921 0.151 0.337II 5 0.628 0.6971 0.295 0.480III 5 0.107 0.749 0.226

1 0.206 0.998IV 5 0.798 0.504 0.821

1 0.110 0.883V 5 0.456 0.9621 0.187 0.105

8

VI 5 0.062 0.0291 0.071 0.880I 5 0.122 0.6771 0.106 0.492II 5 0.347 0.9361 0.134 0.369III 5 0.551 0.740 0.189

1 0.041 0.691IV 5 0.031 0.987 0.7229

V 1 0.193 0.969


117

5 0.547 0.2901 0.078 0.394VI 5 0.252 0.8981 0.063 0.701I 5 0.014 0.3471 0.243 0.779II 5 0.511 0.9071 0.312 0.070III 5 0.163 0.253 0.190

1 0.205 0.975IV 5 0.352 0.962 0.343

1 0.169 0.248V 5 0.714 0.6541 0.023 0.295

10

VI 5 0.023 0.526Table 5-16 . Correlation Between Calculated Parameters; Speed – Flow – Intersection Occupancy

5.8 Capacity Defined by Speed and Flow of Conflict Streams

Capacities at unsignalized intersections under mixed traffic flow with no gap acceptancebehavior have to be developed in a rather specific way. The tendency that the drivers wouldnot stop their vehicles and become more aggressive while they reach the intersection has to betaken into consideration. Drivers tend to maintain their speed rather than stop, therefore,speed is an important value to measure the quality instead of flow. Based on investigations ofeach stream’s speed and flow behavior at its conflict group, there was a strong correlationbetween these parameters. Therefore, speeds of each conflict stream were considered infurther analysis.

Figure 5-36. Stream QC influenced by one Figure 5-37. Stream QC influenced by two conflict stream, QB (I) conflict streams, QA (II) and

QB (I)

The typical conflict of two different streams, QB and QC is described in Figure 5-36. Theconflict of QC running through more than one conflict stream, QA and QB is described inFigure 5-37.

I

QC,VC

QB,VB

I

QC,VC

QB,VB

IIQA,VA


118

The relationships between speed and flow of conflict streams could be described as

CIBIII QcQbaV −−=

CIIAIIIIII QcQbaV −−=

where

VI,VII = Average speed (spot speed) at conflict point I and II [km/h]aI, aII = Constant parameter representing free–flow speed at conflict point I and II [km/h]bI, bII = Speed reduction coefficient caused by flow stream QA and QB [-]cI, cII = Speed reduction coefficient caused by flow stream QC [-]QA, QB, QC = Volume of movements A, B, C [pcu/h]

By using a portion of flow f of each stream i, ( fi) we have

for conflict point I,

CICIII QcQfbaV −⋅−= 1

1fQcQbaV B

IBIII −−=

with C

B

QQf =1

The equations can be solved for the flow rates, QB and QC at conflict point I :

)cf(bVaQ

II

IIC +

−=

1

)fc(b

VaQI

I

IIB

1

+

−=

And for conflict point II we get :

CIICIIIIII QcQfbaV −⋅−= 2

2fQcQbaV A

IIAIIIIII −−=

with C

A

QQf =2

(5-24)(5-25)

(5-26)

(5-27)


119

The flow rates QC and QA are

)cf(bVaQ

IIII

IIIIC +

−=

2

)fc(b

VaQII

II

IIIIA

2

+

−=

Speeds at each conflict point (VI' and VII') have to be observed and measured when streamshave reached their maximum flow (QA-MAX, QB-MAX, QC-MAX). If one of the streams i hasreached its maximum flow Qi-MAX, we might assume that the intersection starts to congestregarding speed VI' and VII' while the other two flows maintain their flow rates (maximumthat has been reached). It is also required that the maximum flow of each stream i should behigher or equal to zero, Qi ≥ 0.0. Due to an ideal relationship between speed and flow (e.g.Equation 5-24 ff.) which might not be fulfilled and the linear equation might have more thantwo dimensions when the group of conflict consists of more than two streams, see also Table5-11 and Figure 5-37, therefore the following argument can be seen in Figure 5-38.

Figure 5-38. Scheme of Speed – Flow Performance of Two Conflict Streams, QA and QC

Figure 5-38 represents the speed – flow relationship for two conflict groups with a pre–defined speed VA' = VC'. Considering Figure 5-37 and Equation 5-25 that each stream in agroup of conflict (II) would have the same average speed, in such circumstances, if thestreams of one conflict reach their maximum flow (capacity), QA, QC with average speed(critical speed), VCRITICAL , other conflicts would produce a negative value, – QA''. In this case,it is required to have the maximum flow (capacity) for all streams, Qi' ≥ 0.0 .

(5-28)

(5-29)

Q [pcu/h] QAQC

V [km/h]

V = f (QC)

- QA`

V = f (QA)

VCRITICAL = VA ̀= VC̀

QC̀

VCRITICAL


120

For further analysis of the maximum flow of the intersection, the following analogy can bemade :

If flow QA has reached its maximum flow,

QA' = QA-MAX and VII = VII'

then

MAXII

MAXAIIIIIIIIIB c

QbVacVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−−−=″ − 0,

and

MAXII

MAXAIIIIIIC c

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=″ − 0,

The total flow of intersection, Qint (1) when QA has reached its maximum flow is

MAXII

MAXAIIIIII

MAXII

MAXAIIIIIIIIIMAXACBA

cQbVa

cQbVacVaQQQQQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−

+⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−−−+=″+″+′=

−

−−

0,

0,)1(int

When flow QB has reached its maximum flow,

QB' = QB-MAX and VI = VI'

then

MAXI

MAXBIIIIIIIIIA c

QbVacVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−−−=″ − 0,

and

MAXI

MAXBIIIC c

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=″ − 0,

The total flow of intersection, Qint (2) when QB has reached its maximum flow is

MAXI

MAXBIII

MAXB

MAXI

MAXBIIIIIIIIICBA

cQbVa

Qc

QbVacVaQQQQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−

++⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−−−=″+′+″=

−

−−

0,

0,)2(int

(5-30)

(5-31)

(5-32)

(5-33)


121

When flow QC has reached its maximum flow, there are two possibilities of maximum flow

of QC ,

QC-MAX = QC' at VI = VI' (conflict point I) and QC-MAX = QC'' at VII = VII' (II)

therefore,

MAXI

BIIIC c

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=′ 0,

MAXII

AIIIIIIC c

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=″ 0,

then the maximum flow of QC is (QC' , QC'')MAX = QC'''

MAXII

CIIIIIIA b

'''QcVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=″ 0,

and

MAXI

CIIIB b

'''QcVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=″ 0,

The total flow of intersection, Qint (3) when QC has reached its maximum flow is

MAXCC

MAXI

CIII

MAXII

CIIIIIICBA

QQ

b'''QcVa

b'''QcVa'''QQQQ

⎟⎠⎞⎜

⎝⎛ ″′

+⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−+

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=+″+″=

,

0,0,)3(int

whereQint (1), Qint (2),Qint (3) = Total maximum flow of intersection based on maximum flow of stream [pcu/h]QA-MAX = Maximum flow of stream A = QA' [pcu/h]QB-MAX = Maximum flow of stream B = QB' [pcu/h]QC-MAX = Maximum flow of stream C = QC' [pcu/h]QA'' = Flow stream A (maximum) while another stream reach its capacity [pcu/h]QB'' = Flow stream B (maximum) while another stream reach its capacity [pcu/h]QC'' = Flow stream C (maximum) while another stream reach its capacity [pcu/h]

= Maximum flow of stream C at second conflict with stream A at VI'' [pcu/h]QC''' = Maximum flow of stream C from two alternatives; QC' and QC'' [pcu/h]

(5-34)

(5-35)


122

VI' = Speed at conflict point I while a stream reaches its capacity [km/h]VII' = Speed at conflict point II while a stream reaches its capacity [km/h]

Since the speed at the maximum flow (capacity) of an intersection is not available or in alimited resource (e.g. intersection–3 which maximum capacity is likely to be reached), thespeed (VI', VII') has to be assumed and would have the same value for all streams and themaximum flow (capacity) of intersection is defined as the minimum value of the total flows[Qint (1), Qint (2), Qint (3)] on the intersection,

MINQ,QQC ],[ )3(int)2(int)1(int≈where

C = Maximum flow (capacity) of the intersection [pcu/h]Qint (1) = Maximum flow of the intersection when QA is maximum, QA-MAX [pcu/h]Qint (2) = Maximum flow of the intersection when QB is maximum, QB-MAX [pcu/h]Qint (3) = Maximum flow of the intersection when QC is maximum, QC-MAX [pcu/h]

This study has found difficulties in measuring speed at conflict points (e.g. conflict point I andII) because only an ordinary camcorder was used for observation. Such instruments couldonly measure space mean speed which was measured as vehicles’ total travel time over astretch of road. Speeds of each stream flow (VA, VB, VC) were observed, see Figure 5-39below. Instead of the speed at conflict point I and II, QC would have an average speed throughconflict streams QA and QB.

Figure 5-39. Stream QC conflicts with two other streams, QA and QB

(5-36)

QA,VA

QC influenced by two other streams ; QA and QB

QB,VB

QC,VC


123

At the maximum volume QC = QC-MAX of movement C it is assumed for the corresponding

speed VC' that

[ ] MAXCABCCC

MAXCAMAXCBMAXCCCC

AABBMAXCCCC

Q)f(b)f(bbaV

)Qf(b)Qf(bQbaV

QbQbQbaV

−

−−−

−

⋅+⋅+−=′⋅⋅−⋅⋅−−=′

−−−=′

21

21

where

C

A

C

B

QQf

QQf == 21 ;

then

[ ]MAX

ABC

CCMAXC )f(b)f(bb

VaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

⋅+⋅+

′−=− 0,

21

( ) ( ){ }MAXMAXC

MAXA

MAXCBMAXCCCCA fQ

bQfbQbVaQ 0,0, 2

1 ⋅=⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ⋅⋅−−′−=′ −

−−

( ) ( ){ }MAXMAXC

MAXB

MAXCAMAXCCCCB fQ

bQfbQbVaQ 0,0, 1

2 ⋅=⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ⋅⋅−−′−=′ −

−−

The maximum flow of the intersection, Qint (1) is

( )

( )[ ]

MAXABC

CC

MAXB

MAXCAMAXCCCC

MAXA

MAXCBMAXCCCCMAXCBA

)f(b)f(bbVa

bQfbQbVa

bQfbQbVaQQQQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

⋅+⋅+

′−+

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ⋅⋅−−′−

+⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ⋅⋅−−′−=+′+′=

−−

−−−

0,0,

0,

21

2

1)1(int

At the maximum volume QA = QA-MAX for movement A the speed VA' , can be expressed as :

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+−=′

⎟⎟⎠

⎞⎜⎜⎝

⎛⋅−−=′

−−=′

−

−−

−

2

2

fbbQaV

fQbQbaV

QbQbaV

CAMAXAAA

MAXACMAXAAAA

CCMAXAAAA

(5-37)

(5-38)

(5-39)


124

then

MAX

CA

AAMAXA

fbb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+

′−=− 0,

2

MAX

MAXA

MAXC

MAXAAAAC f

Qb

QbVaQ⎭⎬⎫

⎩⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=′ −− 0,0,

2

MAXC

MAXB

CCBBB fQ

bQbVaQ

⎭⎬⎫

⎩⎨⎧ ⎟

⎠⎞⎜

⎝⎛ ⋅′=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−−=′ 0,0, 1


MAXC

MAXAAAA

MAXB

CCBB

MAX

CA

AACBMAXA

bQbVa

bQbVa

fbb

VaQQQQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−

+⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−−+

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+

′−=′+′+=

−

−

0,

0,0,

2

)2int(

At the maximum volume QB = QB-MAX for movement B the speed VB' is :

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+−=′

⎥⎦

⎤⎢⎣

⎡⋅−−=′

−−=′

−

−−

−

1

1

fbbQaV

fQbQbaV

QbQbaV

CBMAXBBB

MAXBCMAXBBBB

CCMAXBBBB

then

MAX

CB

BBMAXB

fbb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+

′−=− 0,

1

(5-40)

(5-41)


125

MAX

MAXB

MAXC

MAXBBBBC f

Qb

QbVaQ⎭⎬⎫

⎩⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−=′ −− 0,0,

1

MAXC

MAXA

CCAAA fQ

bQbVaQ

⎭⎬⎫

⎩⎨⎧ ⎟

⎠⎞⎜

⎝⎛ ⋅′=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−−=′ 0,0, 2


MAXC

MAXBBBB

MAX

CB

BB

MAXA

CCAACMAXBA

bQbVa

fbb

Vab

QbVaQQQQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ −′−

+

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

⎥⎦

⎤⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛+

′−+

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−−=′++′=

−

−

0,

0,0,

1

)3int(

From those three alternatives for the maximum flow of movements, the capacity of theintersection is defined as

C ≈ [Qint(1), Qint(2), Qint(3)]MIN

5.9 Capacity Analysis for Three–Leg Unsignalized Intersections

Further analysis was made in this study based on observed data at three–leg unsignalizedintersections. This type of intersections contains less conflict streams compared to four–legunsignalized intersections. The study described the intersections which consist of six streams,six conflict points (I, II, III, IV, V, VI), and six groups of conflicts (C – A, C – B, B – C, B –A, A – C, A – B) (see also Table 5-1 and Figure 5-40). Previously, it has been discussed thatobservation could only measure the average speed of each stream that by unusualmeasurement techniques only the average speed of each movement while crossing theintersection can be estimated. Therefore, the new empirically based method relies on theaverage speed of subject streams and the volume of each stream to determine the capacity asthe maximum possible volume at the intersection.

(5-42)

(5-43)


126

Figure 5-40. Scheme of Conflict Points and Streams at Three–Leg Intersection

As an important parameter, speed and flow of each stream were measured and analyzed for allintersections. Each of them was observed on the basis of each group of conflict. Speed andflow descriptions of each conflict point are :

The following coefficients are defined :

B-A

C-A

QQf =1 ,

A-B

C-B

QQf =2 ,

A-C

B-C

QQf =3 ,

B-A

C-B

QQf =4 ,

C-B

A-C

QQf =5 ,

B-A

A-C

QQf =6

then for each conflict point the model is described by a set of equations.

At the conflict point I,

ABIACIII QcQbaV −− −−=

MAX

II

II

AC

fcb

VaQ

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

⎟⎠⎞⎜

⎝⎛ ′−

=− 0,

1

)1(

( )MAX

AC

MAX

II

II

AB fQ

cfb

VaQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬

⎫

⎪⎩

⎪⎨

⎧

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+⋅

⎟⎠⎞⎜

⎝⎛ ′−

= −− 0,0,

1

)1(

1

)1(

(5-44)

C A

I

II

III

IV

VVI

QC-A

QC-B

Q A-C

Q A-B

QB-A

Q B-C

B


127

At the conflict point II,

BAIIBCIIIIII QcQbaV −− −−=

MAX

IIII

IIII

BC

fcb

VaQ

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

⎟⎠⎞⎜

⎝⎛ ′−

=− 0,

2

)2(

( )MAX

BC

MAX

IIII

IIII

BA fQ

cfb

VaQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬

⎫

⎪⎩

⎪⎨

⎧

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+⋅

⎟⎠⎞⎜

⎝⎛ ′−

= −− 0,0,

2

)2(

2

)2(

At the conflict point III,

CAIIICBIIIIIIIII QcQbaV −− −−=

MAX

IIIIII

IIIIII

CB

fcb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

⎟⎠⎞⎜

⎝⎛ ′−

=− 0,

3

)3(

( )MAX

CB

MAX

IIIIII

IIIIII

CA fQ

cfb

VaQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬

⎫

⎪⎩

⎪⎨

⎧

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+⋅

⎟⎠⎞⎜

⎝⎛ ′−

= −− 0,0,

3

)3(

3

)3(

At the conflict point IV,

ABIVBCIVIVIV QcQbaV −− −−=

MAX

IVIV

IVIV

BC

fcb

VaQ

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

⎟⎠⎞⎜

⎝⎛ ′−

=− 0,

4

)4(

( )MAX

BC

MAX

IVIV

IVIV

AB fQ

cfb

VaQ

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬

⎫

⎪⎩

⎪⎨

⎧

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

+⋅

⎟⎠⎞⎜

⎝⎛ ′−

= −− 0,0,

4

)4(

4

)4(

(5-45)

(5-47)

(5-46)


128

At the conflict point V,

CAVBCVVV QcQbaV −− −−=

( )MAX

VV

VV

BC fcb

VaQ

⎪⎭

⎪⎬

⎫

⎪⎩

⎪⎨

⎧

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⋅+

⎟⎠⎞⎜

⎝⎛ ′−

=− 0,5

)5(

( ){ }MAXBC

MAX

VV

VV

CA fQc

fb

VaQ 0,0, 5

)5(

5

)5( ⋅=

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

⎟⎠⎞⎜

⎝⎛ ′−

= −−

At conflict point VI,

CAVIABVIVIVI QcQbaV −− −−=

( )MAX

VIVI

VIVI

AB fcb

VaQ

⎪⎭

⎪⎬

⎫

⎪⎩

⎪⎨

⎧

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⋅+

⎟⎠⎞⎜

⎝⎛ ′−

=− 0,6

)6(

( ){ }MAXAB

MAX

VIVI

VIVI

CA fQc

fb

VaQ 0,0, 6

)6(

6

)6( ⋅=

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

⎟⎠⎞⎜

⎝⎛ ′−

= −−

For the subject stream QC-A to reach its maximum flow, QC-A(1) with conflict speed VI

(1),VII

(1) , VIII

(1) , VIV (1), VV

(1), VVI (1), the maximum flow of the intersection is calculated as

( )

MAX

II

IIAC

fcb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−=− 0,

1

)1()1(

)1()1()1(

(5-49)

(5-48)


129

( )( ) ( )

( )( )

MAXMAXVV

VV

MAX

IVIV

IVIV

MAX

IIII

IIII

MAXBCBCBCMAXBC

fcbVa

fcb

Va

fcb

Va

QQQQ

⎟⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

⋅+−

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−

== −−−−

0,

,0,,0,

,,

5)1()1(

)1()1(

4

)1()1(

)1()1(

2

)1()1(

)1()1(

)5()4()2(,

( )

MAX

IIIIII

IIIIIICB

fcb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−=− 0,

3

)1()1(

)1()1()3(

( )( )

( )( )

( )( )

( )MAXMAXVIVI

VIVI

MAXIVIV

IVIV

MAXII

II

MAXABABABMAXAB

fcbVa

cfbVa

cfbVa

QQQQ

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

⋅+−

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

+⋅−

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

+⋅−

== −−−−

0,

,0,,0,

,,

6)1()1(

)1()1(

)1(4

)1(

)1()1(

)1(1

)1(

)1()1(

)6()4()1(,

( )

( )( )

( )

( )

MAXMAX

VIVI

VIVI

MAX

VV

VV

MAXIIIIII

IIIIII

MAXCACACAMAXCA

cf

bVa

cf

bVa

cfbVa

QQQQ

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

+⋅−

== −−−−

0,

,0,,0,

,,

)1(

6

)1(

)1()1(

)1(

5

)1(

)1()1(

)1(3

)1(

)1()1(

)6()5()3(,

( )( )

MAXIIII

IIIIBA cfb

VaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

+⋅−

=− 0,)1(2

)1(

)1()1()2(

The maximum flow of the intersection whose subject stream QC-A reaches its maximum flowwhich we call as alternative–1 is

)2(,,

)3(,

)1(1 BAMAXCAMAXABCBMAXBCAC QQQQQQC −−−−−− +++++=

where

QC-A(1) = Maximum flow of stream QC-A as an input flow [pcu/h]

(5-50)


130

Vi(1) = Average speed at conflict point i when QC-A reaches its

maximum flow [pcu/h]ai

(1), bi(1), ci

(1) = Constant at conflict point i when QC-A reaches its maximum flow [-]

For the subject stream QC-B to reach its maximum flow, (QC-B(2) , QC-B

(4), QC-B(5))MAX with

conflict speeds of VI (2), VII

(2), VIII (2), VIV

(2), VV (2), VVI

(2), the maximum flow of the intersectionis calculated as

( )( ) ( )

( )( )

MAXMAXVV

VV

MAX

IVIV

IVIV

MAX

IIII

IIII

MAXBCBCBCMAXBC

fcbVa

fcb

Va

fcb

Va

QQQQ

⎟⎟⎟⎟⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜⎜⎜⎜⎜

⎝

⎛

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

⋅+−

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−

== −−−−

0,

,0,,0,

,,

5)2()2(

)2()2(

4

)2()2(

)2()2(

2

)2()2(

)2()2(

)5()4()2(,

( )

MAX

II

IIAC

fcb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−=− 0,

1

)2()2(

)2()2()1(

( )

MAX

IIIIII

IIIIIICB

fcb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−=− 0,

3

)2()2(

)2()2()3(

( )( )

( )( )

( )( )

( )

MAX

MAXBC

MAXMAXVIVI

VIVI

MAXIVIV

IVIV

MAXII

II

MAXABABABMAXAB

fQ

fcbVa

cfbVa

cfbVa

QQQQ

⎭⎬⎫

⎩⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢

⎣

⎡

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

⋅+−

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

+⋅−

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛

+⋅−

==

−

−−−−

0,

0,

,0,,0,

,,

4

,

6)2()2(

)2()2(

)2(4

)2(

)2()2(

)2(1

)2(

)2()2(

)6()4()1(,


131

( )

( )( )

( )

( )

MAXMAX

VIVI

VIVI

MAX

VV

VV

MAXIIIIII

IIIIII

MAXCACACAMAXCA

cf

bVa

cf

bVa

cfbVa

QQQQ

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

+⋅−

== −−−−

0,

,0,,0,

,,

)2(

6

)2(

)2()2(

)2(

5

)2(

)2()2(

)2(3

)2(

)2()2(

)6()5()3(,

( )( )

MAX

MAXBC

MAXIIII

IIIIBA f

Qcfb

VaQ⎭⎬⎫

⎩⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡

+⋅−

= −− 0,0,

2

,)2(

2)2(

)2()2()2(

The maximum flow of the intersection at alternative–2 whose subject stream QC-B reaches itsmaximum flow is

)2(,,

)3()1(,2 BAMAXCAMAXABCBACMAXBC QQQQQQC −−−−−− +++++=

where

QC-B,MAX = Maximum flow of stream QC-B as an input flow [pcu/h]Vi

(2) = Average speed at conflict point i when QC-B reaches its maximum flow [km/h]ai

(2), bi(2), ci

(2) = Constant at conflict point i when QC-B reaches its maximum flow [-]

By using the same procedure, other alternatives (alternative–3, alternative–4, alternative–5,alternative–6) can be concluded as :

The maximum flow of the intersection at alternative–3 whose QB-C reaches its maximumflow with speed of VB-C

(3),)2(

,,,)1()3(

3 BAMAXCAMAXABMAXBCACCB QQQQQQC −−−−−− +++++=

where

QB-C (3) = Maximum flow of stream QB-C as an input flow

at alternative–3 [pcu/h]Vi

(3) = Average speed at conflict point i when QB-C reaches its maximum flow [km/h]ai

(3), bi(3), ci

(3) = Constant at conflict point i when QB-C reaches its maximum flow [-]

(5-51)

(5-52)


132

The maximum flow of intersection at alternative–4 whose subject stream QB-A reaches itsmaximum flow with speed VB-A

(4) is)2(

,)3(

,)1(

,4 BAMAXCACBMAXBCACMAXAB QQQQQQC −−−−−− +++++=

where

QB-A,MAX = Maximum flow of stream QB-A as an input flow at alternative–4 [pcu/h]Vi

(4) = Average speed at conflict point i when QB-A reaches its maximum flow [km/h]ai

(4), bi(4), ci

(4) = Constant at conflict point i when QB-A reaches its maximum flow [-]

The maximum flow of the intersection at alternative–5 whose subject stream QA-C reaches itsmaximum flow with speed of VA-C

(5) is)2(

,)3(

,)1(

,5 BAMAXABCBMAXBCACMAXCA QQQQQQC −−−−−− +++++=

where

QA-C,MAX = Maximum flow of stream QA-C as an input flow at alternative–5 [pcu/h]Vi

(5) = Average speed at conflict point i when QA-C reaches its maximum flow [km/h]ai

(5), bi(5), ci

(5) = Constant at conflict point i when QA-C reaches its maximum flow [-]

The maximum flow of the intersection at alternative–6 whose subject stream QA-B reaches itsmaximum flow with the speed of VA-B

(6) is

MAXCAMAXABCBMAXBCACBA QQQQQQC ,,)3(

,)1()2(

6 −−−−−− +++++=

where

QA-B(2)

= Maximum flow of stream QA-B as an input flow at alternative–6 [pcu/h]Vi

(6) = Average speed at conflict point i when QA-B reaches its maximum flow [km/h]ai

(6), bi(6), ci

(6) = Constant at conflict point i when QA-B reaches its maximum flow [-]

All possibilities of maximum flows that might occur at each stream were measured one afteranother and the maximum flow (capacity) of the intersection is the least maximum flow(capacity),

C ≈ [C1, C2, C3, C4, C5, C6]MIN

It has been explained previously that it was not possible to observe speed at a conflict point.Therefore, we have considered speeds of each traffic stream which are influenced by other

(5-56)

(5-53)

(5-54)

(5-55)


133

streams of each group of conflict. Thus, we can also take into account the followingequations :

ABABACACACAC QbQbaV −−−−−− −−=

ABABCACABABABCBCBCBC QbQbQbQbaV −−−−−−−−−− −−−−=

CACACBCBCBCB QbQbaV −−−−−− −−=

CACABCBCACACABABABAB QbQbQbQbaV −−−−−−−−−− −−−−=

CBCBABABBCBCCACACACA QbQbQbQbaV −−−−−−−−−− −−−−=

BCBCBABABABA QbQbaV −−−−−− −−=

(see also Table 5-11).

The following flow analysis were described as follows

If streams’ flow QC-A reaches its maximum flow at

⎥⎦

⎤⎢⎣

⎡−−=′ −

−−−−−1

)1()1(

fQbQbaV AC

ABACACACAC

the maximum flow of all streams are

MAX

ABAC

ACACAC

fbb

VaQ

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

′−=

−−

−−− 0,

1

)1(

MAX

AC

MAXAB

ACACACACAB f

Qb

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ −′−= −

−

−−−−− 0,0,

1

)1()1()1(

( ) ( ){ }MAXAB

MAXCABC

ACACABABABABBC Qf

fbbQbQbVaQ 0,0, )1(

45

)1()1()1(

−−−

−−−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡+

−−−=

( ){ }MAXAB

MAXCA

BCBCACACABABABABCA Qf

bQbQbQbVaQ 0,0, )1(

6

)1()1()1()1(

−−

−−−−−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−−−=

MAX

BC

MAXBA

BCBCBABABA f

Qb

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−= −

−

−−−−− 0,0,

2

)1()1()1(

( ){ }MAXCA

MAXCB

CACACBCBCB Qf

bQbVaQ 0,0, )1(

3

)1()1(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=

(5-57)


134

And the maximum flow of the intersection is

)1()1()1()1()1()1()1(int BACAABCBBCACMAXIMUM QQQQQQQ −−−−−−− +++++=

If streams’ flow QC-B reaches its maximum flow at

[ ] ⎥⎦

⎤⎢⎣

⎡−⋅−⎥

⎦

⎤⎢⎣

⎡−−=′ −

−−−−

−−−−−2

)2()2(

54

)2()2(

fQbQfb

fQbQbaV BC

BABCCABC

ABBCBCBCBC

The maximum flows of all streams are

( )MAX

BACA

ABBC

BCBCBC

fbfb

fbb

VaQ

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+⋅++

′−=

−−

−−

−−− 0,

23

4

)2(

MAX

BC

MAXBA

BCBCBABABA f

Qb

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−= −

−

−−−−− 0,0,

2

)2()2()2(

( ){ }MAXBC

MAX

ABCA

BABABCBCBCBCCA Qf

fbb

QaQaVaQ 0,0, )2(5

6

)2()2()2(

−−

−

−−−−−−− ⋅=

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−−′−=

MAX

BC

MAXAB

CACABABABCBCBCBCAB f

Qb

QaQaQaVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ −−−′−= −

−

−−−−−−−−− 0,0,

4

)2()2()2()2()2(

( ){ }MAXCA

MAXCB

CACACBCBCB Qf

bQbVaQ 0,0, )2(

3

)2()2(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=

( ){ }MAXAB

MAXAC

ABABACACAC Qf

bQbVaQ 0,0, )2(

1

)2()2(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=



(5-58)

(5-59)

(5-60)


135

If streams’ flow QB-C reaches its maximum flow at

⎥⎦

⎤⎢⎣

⎡−−=′ −

−−−−−3

)4()4(

fQbQbaV CB

CACBCBCBCB

The maximum flow of all streams,

MAX

CACB

CBCBCB

fbb

VaQ

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

′−=

−−

−−− 0,

3

)3(

MAX

CB

MAXCA

CBCBCBCBCA f

Qb

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ −′−= −

−

−−−−− 0,0,

3

)3()3()3(

MAX

CA

MAX

ABBC

CBCBCACACACABC f

Q

fbb

QbQbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−−−= −

−−

−−−−−−− 0,0,

5

)3(

4

)3()3()3(

MAX

BC

MAXBA

BCBCBABABA f

Qb

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−= −

−

−−−−− 0,0,

2

)3()3()3(

MAX

CA

MAXAB

CBCBBCBCCACACACAAB f

Qb

QbQbQbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−−−= −

−

−−−−−−−−− 0,0,

6

)3()3()3()3()3(

( ){ }MAXAB

MAXAC

ABABACACAC Qf

bQbVaQ 0,0, )3(

1

)3()3(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=



If streams’ flow QB-A reaches its maximum flow at

( ) ( ) ( ))4(6

)4(4

)4(1

)4(ABCAABBCABACABABABAB QfbQfbQfbQbaV −−−−−−−−−− ⋅−⋅−⋅−−=′

(5-61)

(5-62)

(5-63)


136

the maximum flows of all streams are

( ) ( ) ( )MAX

CABCACAB

ABABAB fbfbfbb

VaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡

⋅+⋅+⋅+

′−=

−−−−

−−− 0,

641

)4(

( ){ }MAXAB

MAXAC

ABABACACAC Qf

bQbVaQ 0,0, )4(

1

)4()4(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=

( ) ( ){ }MAXAB

MAXCABC

ACACABABABABBC Qf

fbbQbQbVaQ 0,0, )4(

45

)4()4()4(

−−−

−−−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡

⋅+−−′−

=

( ){ }MAXAB

MAXCA

BCBCACACABABABABCA Qf

bQbQbQbVaQ 0,0, )4(

6

)4()4()4()4(

−−

−−−−−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ −−−′−=

( ){ }MAXCA

MAXCB

ABABBCBCCACACACACB Qf

bQbQbQbVaQ 0,0, )4(

3

)4()4()4()4(

−−

−−−−−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−−−=

MAX

BC

MAXBA

BCBCBABABA f

Qb

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−= −

−

−−−−− 0,0,

2

)4()4()4(



If streams’ flow QA-C reaches its maximum flow at

( ))5(3

6

)5(

5

)5()5(

)5(

CACBCA

ABCA

BCCACACA

CBCBABABBCBCCACACACA

Qfbf

Qbf

QbQba

QbQbQbQbaV

−−−

−−

−−−−

−−−−−−−−−−

⋅−⎟⎟⎠

⎞⎜⎜⎝

⎛−⎟⎟

⎠

⎞⎜⎜⎝

⎛−−=

−−−−=′

the maximum flow of all streams are

( )MAX

CBABBC

CA

CACACA

fbf

bf

bb

VaQ

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⋅+++

′−=

−−−

−

−−− 0,

365

)5(

( ){ }MAXCA

MAXCB

CACACBCBCB Qf

bQbVaQ 0,0, )5(

3

)5()5(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=

(5-65)

(5-64)


137

MAX

CA

MAX

ABBC

CBCBCACACACABC f

Q

fbb

QbQbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎪⎭

⎪⎪⎬

⎫

⎪⎪⎩

⎪⎪⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−−′−= −

−−

−−−−−−− 0,0,

5

)5(

4

)5()5()5(

MAX

CA

MAXAB

CBCBBCBCCACACACAAB f

Qb

QbQbQbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ −−−′−= −

−

−−−−−−−−− 0,0,

6

)5()5()5()5()5(

MAX

BC

MAXBA

BCBCBABABA f

Qb

QbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−= −

−

−−−−− 0,0,

2

)5()5()5(

( ){ }MAXAB

MAXAC

ABABACACAC Qf

bQbVaQ 0,0, )5(

1

)5()5(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=



If streams’ flow QA-B reaches its maximum flow

( ))6(2

)6(BABCBABABABA QfbQbaV −−−−−− ⋅−−=′

the maximum flows of all streams are

( )MAX

BCBA

BABABA fbb

VaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡

⋅+

′−=

−−

−−− 0,

2

)6(

( ){ }MAXBA

MAXBC

BABABABABC Qf

bQbVaQ 0,0, )6(

2

)6()6(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ −′−=

( ){ }MAXBC

MAX

ABCA

BABABCBCBCBCCA Qf

fbb

QbQbVaQ 0,0, )6(5

6

)6()6()6(

−−

−

−−−−−−− ⋅=

⎪⎪

⎭

⎪⎪

⎬

⎫

⎪⎪

⎩

⎪⎪

⎨

⎧

⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢

⎣

⎡

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−−−=

MAX

BC

MAXAB

CACABABABCBCBCBCAB f

Qb

QbQbQbVaQ⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟⎠

⎞⎜⎜⎝

⎛=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−−−= −

−

−−−−−−−−− 0,0,

4

)6()6()6()6()6(

(5-66)

(5-67)


138

( ){ }MAXAB

MAXAC

CACABCBCABABABABAC Qf

bQbQbQbVaQ 0,0, )6(

1

)6()6()6()6(

−−

−−−−−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−−−=

( ){ }MAXCA

MAXCB

CACACBCBCB Qf

bQbVaQ 0,0, )6(

3

)6()6(

−−

−−−−− ⋅=

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎦

⎤⎢⎣

⎡ −−=

And the maximum flow of the intersection,


The total maximum flow of the intersection is the least maximum flow from all possiblemaximum flows, Qint-MAXIMUM

( )MINMAXIMUMMAXIMUMMAXIMUMMAXIMUMMAXIMUMMAXIMUM QQQQQQC )6(int

)5(int

)4(int

)3(int

)2(int

)1(int.int ,,,,, −−−−−−=

In order to simplify the calculation and performance for data and results, a matrix for capacityanalysis of the total intersection is used (Table 5-17).

Maximum Flows StreamSpeed atMaximum

Flows SubjectStream

MaximumFlows

SubjectStream

QC-A QC-B QB-C QB-A QA-C QA-B

TotalMaximumFlow at

IntersectionVC-A=VC-A' QC-A

(1) QC-A(1) QC-B

(1) QB-C(1) QB-A

(1) QA-C(1) QA-B

(1) Qint(1)

VC-B=VC-B' QC-B(2) QC-A

(2) QC-B(2) QB-C

(2) QB-A(2) QA-C

(2) QA-B(2) Qint

(2)

VB-C=VB-C' QB-C(3) QC-A

(3) QC-B(3) QB-C

(3) QB-A(3) QA-C

(3) QA-B(3) Qint

(3)

VB-A=VB-A' QB-A(4) QC-A

(4) QC-B(4) QB-C

(4) QB-A(4) QA-C

(4) QA-B(4) Qint

(4)

VA-C=VA-C' QA-C(5) QC-A

(5) QC-B(5) QB-C

(5) QB-A(5) QA-C

(5) QA-B(5) Qint

(5)

VA-B=VA-B' QA-B(6) QC-A

(6) QC-B(6) QB-C

(6) QB-A(6) QA-C

(6) QA-B(6) Qint

(6)

Maximum Flow of Intersection = Qint(i)

MIN

Table 5-17 . Matrix of Maximum Flow (Capacity) of Intersection

Table 5-17 could be used as a standard matrix of maximum flow analysis based on speed andflow of conflict streams. For further analysis, we could apply such formulas as in Equation5-57 to Equation 5-69. For example, with using data measurement from intersection–1 thefollowing calculation could be conducted.

At conflict group–I :

From the field measurement we have

QA-C = 639.2 pcu/h , QA-B = 350.2 pcu/h , QB-A = 272.2 pcu/h , QB-C = 222.3 pcu/h,

QC-A = 570.3 pcu/h , QC-B = 168.2 pcu/h

ABACAC Q.Q..V −−− −−= 148032052134

(5-68)

(5-69)


139

The portion of flow is

ACAB

C-A

B-A

QQQQf

−− =

==

477.03.5702.272

1

Thus,

MAX

ACAC

ACAC

VQ

QV

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−=

−=′

−−

−−

0,39.0

521.34

39.0521.34

)1(

)1(

By using QC-A(1) from the calculation (input data), and with previous formulas we can easily

find other maximum flows : QB-A(1), QC-B

(1), QA-C(1), QA-B

(1), QB-C(1). The total maximum flow

is then

( ) ( ) ( ) ( ) ( )1111)1(1 0,39.0

521.34BACAABCBBC

MAX

AC QQQQQVC −−−−−− +++++

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−=

At conflict group–II :


2.2722.168;

2.1682.639;

2.3502.168

432 ======−

−

−

−

−

−

AB

BC

BC

CA

BA

BC

QQf

QQf

QQf

BCABBCCABCBA QQQQQQ −−−−−− === 618.1;800.3;082.2

Thus,

MAX

BCBC

BCBC

VQ

QV

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−=

−=′

−−

−−

0,44.1

189.18

44.1189.18

)2(

)2(

By using QC-B(2) (input data) from the calculation, and with previous formulas we can easily

find other maximum flows, QA-B(2), QA-C

(2), QB-A(2), QB-C

(2), QC-A(2). The total maximum flow is

( ) ( ) ( ) ( ) ( ) ( )222222 0,44.1

189.18BACAABCB

MAX

BCAC QQQQVQC −−−−

−− ++++

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−+=

A-BA-CB-AC-BC-B Q.Q.Q.Q..V 229008402680211018918 −−−−=


140

At conflict group–III :

CACBCB QQV −−− −−= 091.0288.0880.18


2.6393.222

5 ==−

−

CA

CB

QQf

CBCA QQ −− = 875.2

Thus,

MAX

CBCB

CBCB

VQ

QV

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−=

−=′

−−

−−

0,549.0

880.18

549.0880.18

)3(

)3(

By using QB-C(3) (input data) from the calculation, and with previous formulas we can easily

find other maximum flows, QA-C(3), QC-B

(3), QA-B(3), QB-A


( ) ( ) ( ) ( ) ( ) ( )333333 0,549.0

880.18BACAAB

MAX

CBBCAC QQQVQQC −−−

−−− +++

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−++=

At conflict group–IV :


2.2722.639;

2.2722.168;

3.5702.272

641 ======−

−

−

−

AB

CA

AB

BC

C-A

B-A

QQf

QQf

QQf

ABCAABBCABAC QQQQQQ −−−−−− === 348.2;618.0;095.2

Thus,

MAX

ABAB

ABAB

VQ

QV

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−=

−=′

−−

−−

0,395.0

802.29

395.0802.29

)4(

)4(

By using QB-A(4) (input data) from the calculation, and with previous formulas we can easily

find other maximum flows, QC-A(4), QC-B

(4), QA-C(4), QB-C

(4), QA-B(4). The total maximum flow is

( ) ( ) ( ) ( ) ( ) ( )444444 0,395.0

802.29BACA

MAX

ABCBBCAC QQVQQQC −−

−−−− ++

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−+++=

A-CB-AC-BC-AB-A Q.Q.Q.Q..V 058015700140045080229 −−−−=


141

At conflict group–V :


2.2722.639;

2.6393.222;

2.1682.639

653 ======−

−

−

−

−

−

AB

CA

CA

CB

BC

CA

QQf

QQf

QQf

CAABCACBCABC QQQQQQ −−−−−− === 426.0;348.0;263.0

Thus,

MAX

CACA

CACA

VQ

QV

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−=

−=′

−−

−−

0,619.0

657.31

619.0657.31

)5(

)5(

By using QA-C(5) (input data) from the calculation, and with previous formulas we can easily

find other maximum flows, QB-C(5), QC-B

(5), QB-A(5), QA-B


( ) ( ) ( ) ( ) ( ) ( )555555 0,619.0

657.31BA

MAX

CAABCBBCAC QVQQQQC −

−−−−− +

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−++++=

At conflict group–VI :


2.3502.168

2 ==−

−

BA

BC

QQf

BABC QQ −− = 480.0

Thus,

MAX

BABA

BABA

VQ

QV

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−=

−=′

−−

−−

0,572.0

387.29

572.0387.29

)6(

)6(

By using QA-B(6) (input data) from the calculation, and with previous formulas we can easily

find other maximum flows, QC-B(6), QA-C

(6), QB-A(6), QC-A

(6), QB-C(6). The total maximum flow is

( ) ( ) ( ) ( ) ( ) ( )

MAX

BACAABCBBCAC

VQQQQQC⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛ ′−+++++= −

−−−−− 0,572.0

387.29666666

There are six alternatives of maximum flows (capacities) which were based on an assumptionthat each speed has reached its maximum flow (capacity), QC-A

(1), QC-B(2), QB-C

(3), QB-A(4),

QA-C(5), QA-B

(6).

A-CB-AB-CC-BA-C Q.Q.Q.Q..V 31102650440161065731 −−−−=

A-BC-BA-B Q.Q..V 5060014038729 −−=


142

The total maximum flow (capacity) of the intersection isCintersection = {C(1), C(2), C(3), C(4), C(5), C(6)}MIN

A calculation pattern {follow the number : [1], [2], [3], [4], [5]} of each stream is presentedin Table 5-18. The results are presented with the following table/matrix at Table 5-19.

Maximum Flows’ StreamSpeed atMaximum

Flows’ SubjectStream

MaximumFlows’SubjectStream


TotalMaximumFlow at

Intersection

VC-A=VC-A' QC-A(1) QC-A

(1) QC-B(1) QB-C

(1) QB-A(1) QA-C

(1) QA-B(1)

VC-B=VC-B' QC-B(2) QC-A

(2) QC-B(2) QB-C

(2) QB-A(2) QA-C

(2) QA-B(2)

VB-C=VB-C' QB-C(3) QC-A

(3) QC-B(3) QB-C

(3) QB-A(3) QA-C

(3) QA-B(3)

VB-A=VB-A' QB-A(4) QC-A

(4) QC-B(4) QB-C

(4) QB-A(4) QA-C

(4) QA-B(4)

VA-C=VA-C' QA-C(5) QC-A

(5) QC-B(5) QB-C

(5) QB-A(5) QA-C

(5) QA-B(5)

VA-B=VA-B' QA-B(6) QC-A

(6) QC-B(6) QB-C

(6) QB-A(6) QA-C

(6) QA-B(6)

Maximum Flow (Capacity) of Intersection = Σ Qi(j)

MIN

Table 5-18. Flow Pattern for Matrix of Maximum Flow

[1][2]

[3][4] [5]

[1][2]

[3][4][5]

[1][2]

[3][4][5]

[1][2] [3]

[4][5]

[1][2]

[3] [4][5]

[1][2]

[3][4]

[5]

Σ Qi(1)

Σ Qi(2)

Σ Qi(3)

Σ Qi(4)

Σ Qi(5)

Σ Qi(6)


143


144

5.10 Conclusions

Each of the movements at an intersection has been observed related to their speed and flow.By those two parameters, investigations have been made further at any correlation betweenconflict streams (six streams; C – A, C – B, B – C, B – A, A – C, A – B) as defined bygroup of conflicts (I, II, III, IV, V, VI). Results of the relation between parameters show that asuitable correlation between speed and flow of conflict groups could be developed(corresponds to the R2 and standard error, SE values) even if there was only a small correlationat some groups, e.g. conflict group–IV, stream B – A (which is thought to be an impact of thelack of lane discipline that drivers tend to use other lane paths passing through theintersection with the consequence of higher speed). It was also found that a good estimationof the relation between volumes of streams and intersection occupancy could be obtained,especially with 5–minute intervals of observation.

Further analysis has taken into account the speed at conflict points or the speed of eachmovement through the intersection. The volume of each movement is the most importantparameter to calculate the maximum flow (capacity) based on the conflict streams. Maximumflows of each stream were found to correspond to the speed and flow of other streams at agroup of conflict. There are six (6) alternatives of maximum flows at the intersection becausethe maximum flow of each stream has to be counted.

Each stream was observed related to its speed and flow at its own group of conflict. Theory ofconflict was then adopted in analysis. It was assumed that each stream has reached itsmaximum flow, Qi

j (stream i and alternative j) at the smallest speed, Vi'. When one streamreaches its maximum flow, e.g. QC-A

(1), VC-A' means that other streams would not meet their(real) maximum flow (QC-B

(2), QB-C(3), QB-A

(4), QA-C(5), QA-B

(6)) and their (real) speed (VC-B',VB-C', VB-A', VA-C', VA-B'). By using the value of maximum flow, e.g. QC-A

(1) , the speed, VC-A'and the streams’ speeds VC-B, VB-C, VB-A, VA-C, VA-B, other streams’ flow, QC-B

(1), QB-C(1),

QB-A(1), QA-C

(1), QA-B(1) can easily be calculated from the regression equations.

6 Traffic Quality and Performance

145


6.1 Introduction

Results of data performance have been presented and possibilities of the new approach havebeen discovered so far. The approach performed another way to calculate the capacity basedon conflicting streams interactions. This is based on the relation of speed and flow for eachstream in its group of conflicts. A large number of data from ten observed three–legunsignalized intersections was used to develop the model. The model could have an ability toestimate each stream performance while the maximum flow (capacity) has been reached at acertain speed. In order to count the maximum flow, all possibilities of maximum flow, thatassumed each stream has reached its maximum flow, must be taken into account. It wasrecommended to use such a matrix of probabilities.

The following chapter would perform the maximum flow of all intersections based on the newapproach. The results are then calibrated with the capacity analyzed from the manual (IHCM,1997) in order to see how different the capacities from both methods are and at which speedthe intersections reached their capacity. This chapter will also investigate how different (inpercentage) the intersections were occupied by measured vehicles (field data) and by themaximum flow that has been calculated.

6.2 Maximum Flow (Capacity) from the conflict streams method

A new approach of capacity measurement based on conflict streams has been created andinvestigated in the previous chapter. It become clear that the correlation between speed andflow of streams could be used to develop such an approach of conflict streams. The maximumflow is assumed to be reached by one of the traffic streams while the traffic flow at anintersection is congested, but traffic movements are still possible. When stream i reaches itsmaximum flow (capacity) j, the total capacity of the intersection is the sum of the maximumflow Ci of all streams,

∑=

=6

1j

jitotal CC

whereCtotal = Total maximum stream volumes at the intersection [pcu/h]Ci

j = Maximum flow of stream i when subject stream j reaches its maximum flow [pcu/h]

i = 1 for movement C – A= 2 for movement C – B= 3 for movement B – C= 4 for movement B – A= 5 for movement A – C= 6 for movement A – B

(6-1)


146

Each intersection has been investigated and measured and it is required to have all data fromthe intersections in order to develop a general equation (model) which is suitable for allintersections. The model (1–minute interval observations) can be concluded in the followingTable 6-1.

SubjectStream Maximum Flows of Subject Stream Total Maximum

Flows Stream

QC-A(1)

= CC-A

MAX

C-AB-A

.VQ..

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ′−− 0,1910

075009518 ∑=

=6

1

)1(

iitotal CC

QC-B(2)

= CC-B

MAX

BCBACAAB

.VQ.Q.Q..

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ′−+−− −−−− 0,0690

11302340357094919 ∑=

=6

1

)2(

iitotal CC

QB-C(3)

= CB-C

MAX

CBCA

.VQ..

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ′−+ −− 0,3970

033079913 ∑=

=6

1

)3(

iitotal CC

QB-A(4)

= CB-A

MAX

ABCABCAC

.VQ.Q.Q..

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ′−−+− −−−− 0,1790

33704430095006416 ∑=

=6

1

)4(

iitotal CC

QA-C(5)

= CA-C

MAX

CAABCBBC

.VQ.Q.Q..

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ′−−−− −−−− 0,1490

2050015006012917 ∑=

=6

1

)5(

iitotal CC

QA-B(6)

= CA-B

MAX

BABC

.VQ..

⎪⎭

⎪⎬⎫

⎪⎩

⎪⎨⎧

⎥⎥⎦

⎤

⎢⎢⎣

⎡ ′−+ −− 0,2100

207083515 ∑=

=6

1

)6(

iitotal CC

Maximum Flow (Capacity) of Intersection =MINj

jiCC ⎥

⎦

⎤⎢⎣

⎡= ∑

=

6

1.int

Table 6-1. Model for The Maximum Flow (Capacity) of Each Stream of Intersection

In order to complete the measurement above, data of speed and flow of each stream have beenprovided in the previous chapter, Appendix B and Appendix C. Further analysis is guided bythe following table of matrix of maximum flow and Equation 5-57 to Equation 5-69.

Maximum Flows StreamSpeed atMaximum

Flows SubjectStream

MaximumFlows

SubjectStream


TotalMaximumFlow at

IntersectionVC-A' QC-A

(1) CC-A QC-B(1) QB-C

(1) QB-A(1) QA-C

(1) QA-B(1) Σ Qi

(1)

VC-B' QC-B(2) QC-A

(2) CC-B QB-C(2) QB-A

(2) QA-C(2) QA-B

(2) Σ Qi(2)

VB-C' QB-C(3) QC-A

(3) QC-B(3) CB-C QB-A

(3) QA-C(3) QA-B

(3) Σ Qi(3)

VB-A' QB-A(4) QC-A

(4) QC-B(4) QB-C

(4) CB-A QA-C(4) QA-B

(4) Σ Qi(4)

VA-C' QA-C(5) QC-A

(5) QC-B(5) QB-C

(5) QB-A(5) CA-C QA-B

(5) Σ Qi(5)

VA-B' QA-B(6) QC-A

(6) QC-B(6) QB-C

(6) QB-A(6) QA-C

(6) CA-B Σ Qi(6)

Maximum Flow (Capacity) of Intersection = Σ Qi(j)

MINTable 6-2. Matrix of Maximum Flow (Capacity) of Intersection


147

Observations at each intersection would create their own equations of maximum flow withmeasurements from the field, e.g. streams’ speed at the highest flow. However, it was notalways possible to have a speed at the maximum streams’ flow since field observationcovered only two hours for each intersection. Therefore, estimations of speed at the maximumflow had to made for a further calculation which these speeds varied between 10 km/h and 15km/h by considering previous studies at the current intersections. An example of capacityanalysis on intersection–1 (data) is given in the following Table 6-3 and for speed values of11 km/h and 12 km/h Table 6-4 and 6-5 are applicable. For values of speed of 10 km/h, 13km/h, 14 km/h and 15 km/h see Appendix D. The following Table 6-4 and Table 6-5 show thecapacities which are close to the results from the manual with speeds of 12 km/h and 13 km/hwhile calculations for other intersections are given in Appendix D.

Speed atMaximum

FlowMaximum

Flow QC-A QC-B QB-C QB-A QA-C QA-B Σ Qi(j)

VC-A' QC-A(1) 1923.16 0.00 0.00 917.91 0.00 875.93 3716.99

VC-B' QC-B(2) 2347.69 0.00 0.00 0.00 0.00 3484.62 5832.31

VB-C' QB-C(3) 2347.69 982.31 0.00 0.00 0.00 848.75 4178.75

VB-A' QB-A(4) 2343.02 0.00 0.00 10.08 0.00 875.93 3229.04

VA-C' QA-C(5) 1960.13 412.16 0.00 837.96 935.70 864.53 5010.48

VA-B' QA-B(6) 2347.69 415.19 0.00 0.00 0.00 864.44 3627.32

Capacity of Intersection = 3229.04Table 6-3. Capacity Intersection (Data) from Intersection–1

Speed atMaximum

FlowMaximum


11 QC-A(1) 1877.01 0.00 423.02 895.88 0.00 1381.43 4577.34

11 QC-B(2) 1767.17 412.80 558.28 1175.60 1627.18 4931.19 10472.23

11 QB-C(3) 1863.47 309.48 555.89 930.35 1598.39 1686.49 6944.08

11 QB-A(4) 2197.66 96.54 660.61 79.30 2858.22 1476.59 7368.91

11 QA-C(5) 2162.96 1871.46 541.82 167.67 1429.21 3226.15 9399.27

11 QA-B(6) 1905.45 1260.05 554.65 823.44 1583.46 2623.48 8750.53

Capacity of Intersection = 4577.34Table 6-4. Capacity Intersection (Model with speed, v = 11 km/h) from Intersection–1

Speed atMaximum

FlowMaximum


12 QC-A(1) 1612.46 0.00 271.89 769.61 0.00 1095.71 3749.67

12 QC-B(2) 1479.87 366.67 399.28 1107.26 1532.58 4885.72 9771.39

12 QB-C(3) 1500.53 381.13 357.28 1054.66 1027.33 1471.40 5792.33

12 QB-A(4) 1889.67 63.19 478.18 63.64 2481.69 1158.00 6134.36

12 QA-C(5) 1859.01 1581.83 371.31 141.72 1196.02 2654.95 7804.84

12 QA-B(6) 1636.08 999.44 371.58 709.45 1199.32 2080.87 6996.75

Capacity of Intersection = 3749.67Table 6-5. Capacity Intersection (Model with speed, v = 12 km/h) from Intersection–1


148

Average speeds between 10 km/h and 13 km/h were used to predict the capacity of anintersection which is considered from observations of intersections, e.g. intersection–3, whereits maximum flow was likely to be reached. There was a total number of 4902 vehicles perhour and various width of legs: 9.6 m, 6.5 m and 8.0 m, total average speed 15.6 km/h formotorized vehicles (11.3 km/h for cars) and 5.8 km/h for non–motorized vehicles. Therefore,this intersection could be used as a reference for the speed. Also, experience from a previousstudy by MINISTRY OF PUBLIC WORKS (1999) – KALIMANTAN URBANDEVELOPMENT PROJECT (KUDP) has conducted an investigation at intersection–2 whichreached its maximum flow (capacity) with a degree of saturation of 0.969 (delay=17.73sec/pcu) in peak hour morning and 0.863 (delay=14.52 sec/pcu) in peak hour evening. Thiscould give an indication related to average speed at the maximum flow at an intersection.

6.3 Capacity Calibrated

In the previous chapter, we have given a brief description on fundamental basic understandingof the Indonesian highway capacity manual. Capacity at unsignalized intersections by themanual is defined as a result of basic capacity within ideal traffic conditions related tovarious adjustment factors and corrections which consider the impact of road environment,geometric design of road and traffic conditions. As it is defined in the INDONESIANHIGHWAY CAPACITY MANUAL (1997), capacity can be calculated as :

MIRTLTRSUCSMW FFFFFFFCC ⋅⋅⋅⋅⋅⋅⋅= 0

where

C = Capacity [pcu/h]C0 = Base capacity [pcu/h]FW = Adjustment factor for width of approach [-]FM = Adjustment factor for median at major road [-]FCS = Adjustment factor for city size [-]FRSU = Adjustment factor for type of environment, side friction and

non–motorized [-]FLT = Adjustment factor for left–turn [-]FRT = Adjustment factor for right–turn [-]FMI = Adjustment factor for ratio of traffic at minor road [-]

The required parameters, e.g. base capacity and adjustment factors were performed by dataobservation (environment, traffic and geometry) in chapter five and Appendix A. Thefollowing Table 6-6 below performed all adjustment factors for intersections based on a fieldinvestigation and measurements. Since there are no other methods of capacity calculationwhich are suitable for Indonesia, the manual is recently used for planning and designpurposes. Therefore, results of capacity defined by the manual are necessarily used tocalibrate those from the new method of capacity conflict. Capacities of each intersection aregiven in Table 6-7.

(6-2)


149

Adjustment FactorsIntersection C0

[pcu/h] FW FM FCS FRSU FLT FRT FMI

1 3200 1.008 1.00 0.94 0.94 1.399 0.937 0.8822 2900 1.027 1.00 1.00 0.94 1.270 0.952 0.9033 2700 1.035 1.00 0.94 0.94 0.965 1.068 1.3794 2700 0.965 1.00 0.94 0.97 1.321 0.901 0.9215 2700 1.065 1.00 0.94 0.97 1.092 1.069 1.1016 3200 0.975 1.00 0.94 0.94 1.237 0.934 1.0117 2700 1.077 1.00 0.94 0.97 1.185 1.005 0.9448 2700 0.951 1.00 0.94 0.98 1.479 0.968 0.8519 2700 1.007 1.00 0.94 0.98 1.174 0.964 0.95110 2700 1.055 1.00 1.00 0.97 1.380 0.899 0.849

Table 6-6. Adjustment Factors for Capacity Based on Empirical Approach

Intersection Type of Intersection Capacity [pcu/h]1 324 3273.602 342 2980.153 322 3393.414 322 2703.535 322 3167.206 324 3306.437 322 3273.608 322 3393.419 322 2703.53

10 322 3167.20Table 6-7. Total Capacity Intersection Calculated by the IHCM (1997) Method

Based on the capacity calculation, both methods are compared in the following graph inFigure 6-1. In order to give an overview of the capacity analysis, results from eachintersections’ data analysis and their average are performed. The type of data analysis meansthat each data resource (speed and flow) from each intersection was used to find a maximumflow. Speeds range from 10 km/h to 13 km/h were applied for intersections at which themaximum flow was reached. The figure shows that analysis from data performed very closeto the results from the manual, but some, e.g. intersection–3, intersection–7 and intersection–8contributed small differences (compared to the manual). The reasons might be an over–estimation in speed. In such a case, it might be necessary to consider more accurate fieldobservations and measurements conducted with a special apparatus/camera (speed and flowdetector).

Intersection/Approximate Speed to the Maximum Flow [km/h]Type ofAnalysis 1 2 3 4 5 6 7 8 9 10

DATA 22 26 10 11 16 10 17 6 13 11

MODEL 12.6 13.1 10.3 13.6 10.6 12.8 10.6 10.5 13.2 11.3Table 6-8. Approximate Speed to the Maximum Flow (results close to the manual)


150

Figure 6-1. Capacity Calibrated Data – IHCM – Model

Figure 6-2. Capacity Calibrated Data – IHCM – Model (v = 10 km/h – 13 km/h)

CAPACITY CALIBRATION

0500

1000150020002500300035004000450050005500600065007000

1 2 3 4 5 6 7 8 9 10

Intersection

Cap

acity

[pcu

/h]

DATA

AVERAGE DATA

IHCM-1997

MODEL v=10 km/h

MODEL v=11 km/h

MODEL v=12 km/h

MODEL v=13 km/h

MODEL v=14 km/h

MODEL v=15 km/h

CAPACITY CALIBRATION (MODEL V = 10 km/h - 13 km/h)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1 2 3 4 5 6 7 8 9 10

Intersection

Cap

acity

[pcu

/h]

DATAIHCMMODEL

V=12.6 km/h

V=13.1 km/h

V=10.3 km/h

V=13.6 km/h

V=10.6 km/h

V=12.8 km/h

V=10.6 km/h

V=10.5 km/h

V=13.2 km/h

V=11.3 km/h


151

The developed model with different speeds is applied (v=10 km/h, v=11 km/h, v=12 km/h,v=13 km/h, v=14 km/h, v=15 km/h), see Figure 6-1. The figures show the capacity for allintersections based on data measurement and the manual. It can be seen that the capacitybased on the manual is between results of the model with speeds’ range 10 km/h – 14 km/hand the results is close to the results from IHCM (1997) at the average speed of 11.86 km/h(Figure 6-2) and we can also see that every intersection remains to have different speeds at amaximum flow close to the results from the manual (see Table 6-8).

Speed, V[km/h] 10 11 12 13 14 15

Capacity,C[pcu/h] 4619.53 3905.10 3208.23 2514.86 1860.50 1331.94

σ[pcu/h] 1024.39 875.94 718.35 558.78 390.78 208.72

Table 6-9. Standard Deviation of Capacity for different speeds, V

In a general view, all intersections would reach their maximum flow with the speeds rangeof 10 km/h – 13 km/h at the maximum flow which is very close to the capacity resulted fromthe manual (Figure 6-2). A standard deviation of the resulting capacities for each speed ispresented in Table 6-9.

6.4 Relationship Between Speed and Flow of Intersection

Results of maximum flows (capacity) at several intersections with various speed levels havebeen presented. The average speed of vehicles at the intersections has a significant impact

Figure 6-3. Relationship Between Speed and Total Flow of Intersection

SPEED AND FLOW RELATIONSHIP

0

2

4

6

8

10

12

14

16

18

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Flow [pcu/h]

Spee

d [k

m/h

]

totalQV 0015.0863.16 −=


152

on total (maximum) flow where small differences of speed would indicate large differences inmaximum flow (mean difference = 575.70 pcu/h, σ = 293.60). While the model wasdeveloped by the portion of streams’ flow, it is then necessary to create a model which issuitable for total flow of the intersection, Qtotal and average speed of the intersection, V. Basedon data from Table 6-9, Figure 6-3 was plotted to show the relationship between speed andflow of unsignalized intersections. It can be concluded that the free–flow speed is found to be16.863 km/h and the speed is decreasing by 1.50 km/h for every 1000 pcu/h.

6.5 Relationship Between Flow and Intersection Occupancy of Intersection

It was described in chapter five correlation between each flow and intersection occupancy.Such an approach is important for traffic flow consisting of vehicles with various static anddynamic dimensions where lane concepts (lane discipline) no longer exist and the widthconcept applies (vehicles’ area = width x length). This technique could give an indication towhich extend area (conflict area) of the intersection is occupied by vehicles at a certain flowrate during a time interval time or how many percent of the conflict area were occupied at thesame time when the maximum flow (capacity) was reached. Furthermore, the relationshipcould also identify whether the traffic follows the common rule of ”lane concept” or ”widthconcept” which both gave an indication on large conflict areas occupied by vehicles. If driverstend to take an opportunity to pass through the intersection by using the ”width concept”, theintersections might be occupied at critical stages while the maximum flow has not yet beenreached. The following Equation 6-3 and Equation 6-4 show the relationships for 1–minuteand 5–minute intervals which are considered to be suitable for all intersections,

For a 1–minute interval we find

BA

CAABCBBCAC

QQQQQQ

−

−−−−−

++++++=

154.0042.0156.0085.0073.0136.0298.3OccupancyonIntersecti

R2 = 0.106 SE = 2.1293

where

IO = Intersection Occupancy [%]QC - A = Traffic flow of stream C – A [pcu/1–min]QC - B = Traffic flow of stream C – B [pcu/1–min]QB - C = Traffic flow of stream B – C [pcu/1–min]QB - A = Traffic flow of stream B – A [pcu/1–min]QA - C = Traffic flow of stream A – C [pcu/1–min]QA - B = Traffic flow of stream A – B [pcu/1–min]

(6-3)


153

And for a 5–minute interval we find

BA

CAABCBBCAC

QQQQQQ

−

−−−−−

−+++++−=

025.0039.0002.0038.0187.0114.0351.0OccupancyonIntersecti

R2 = 0.814 SE = 0.2542

whereIO = Intersection Occupancy [%]QC - A = traffic flow of stream C – A [pcu/5–min]QC - B = traffic flow of stream C – B [pcu/5–min]QB - C = traffic flow of stream B – C [pcu/5–min]QB - A = traffic flow of stream B – A [pcu/5–min]QA - C = traffic flow of stream A – C [pcu/5–min]QA - B = traffic flow of stream A – B [pcu/5–min]

Using Equation 6-3, the flow measurement (each stream) and the predicted maximum flow(capacity) at the speed of 12.0 km/h, are given in Figure 6-4 and Table 6-10. According tothe model, even though the intersection has reached its maximum flow, vehicles haveoccupied not more than 12% of the conflict area (in 1–minute interval observation) withaverage differences of 4.84%. That means drivers in this study have not followed thetendency of using the width concept in order to pass through the intersection while themaximum flow has been reached. This is the reason why the concept for the model wasconsidered to be more accurate that the maximum flow of the intersection is reached bymaximizing one of the streams.

Figure 6-4. Intersection occupancy with data measurement and maximum flow (capacity)

(6-4)

INTERSECTION OCCUPANCY

7,516,44

5,26 4,786,05 6,19

5,554,24 4,28

5,11

11,0210,44 10,14 10,06 9,77

10,88 10,3910,99

9,7210,44

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10

Intersection

Inte

rsec

tion

Occ

upan

cy (%

)

Flow (measurement)Maximum flow (capacity)


154

Intersection Occupancy [%]

Intersection Form Based on FlowMeasurement, Qi

Based onMaximumFlow, Ci

'

1 7.51 11.022 6.44 10.443 5.26 10.144 4.78 10.065 6.05 9.776 6.19 10.887 5.55 10.398 4.24 10.999 4.28 9.7210

3.298 + 0.136 Q C-A + 0.073 Q C-B+ 0.085 Q B-C + 0.156 Q B-A +

0.042 Q A-C + 0.154 Q A-B

5.11 10.44Table 6-10. Intersection occupancy of each intersection with data measurement and maximum flow (capacity) in 1–minute interval

Results from the flow measurement and the maximum flow related to intersection occupancywere then used to develop a suitable model for flow and intersection occupancy appropriatefor all intersections. Figure 6-5 shows that a linear model would give a very good approach toidentify the area of intersection being occupied at a certain level flow rate. Interesting to findthat the intersection might be in 100% occupied when the total traffic flow at intersection wasreached its maximum value of about 1230 pcu/1–minute. It can be concluded from this study,the drivers have not take a hundred percent of an opportunity of using the ”width concept”during their travel across the intersection.

Figure 6-5. Intersection Occupancy and Total of Traffic Flow

FLOW AND INTERSECTION OCCUPANCY

0123456789

1011121314151617181920

0 10 20 30 40 50 60 70 80 90 100 110 120

Flow [pcu/1-minute interval]

Inte

rsec

tion

Occ

upan

cy [%

]

Flow (measurement)

Maximum flow (model)


155

6.6 Delay and Probability of Queue

The Indonesian manual has not given any indication of measuring the performance ofintersections which in general was represented by the level of service (LOS). However, themanual has promoted the delay and the probability of queues as parameters to measure thetraffic performance at unsignalized intersections. This study would adopt the method from themanual to measure the delay and the probability of queue based on the actual maximum flow/capacity from the new approach. Furthermore, a criterion from the HIGHWAY CAPACITYMANUAL (2000) was also used in order to classify the level of service (LOS) of intersectionswhich has also been suggested by the MINISTRY OF PUBLIC WORKS (1998). The level–of–service criteria for AWSC intersections has been adopted to classify the level ofintersections performance as can be seen below.

Level of Service Control Delay [sec/veh]A 0 – 10B > 10 – 15C > 15 – 25D > 25 – 35E > 35 – 50F > 50

Table 6-11. Level of Service (LOS) Criteria for AWSC Intersections (HCM, 2000)

6.6.1 Delay

Another important parameter to measure the quality of traffic flow at the intersection is delay.It is obvious that the delay is depending on the degree of saturation (flow and capacity). Butsince this study had difficulties to investigate the delay at the field because only a very smallpercentage of vehicles stops (0.40% – 0.50%) nor queues were observed and the averagewaiting time was less than 2 seconds, the method was adopted from the manual (IHCM,1997) where the delay was measured based on calculation (calculated delay) and the degree ofsaturation. The study has to adopt the current method of delay calculation from the manualbecause during the field investigations, delay could not be measured. Total delay ofintersections are estimated based on the degree of saturation which is illustrated in Figure 6-6.

The average delay for the whole intersection (sec/pcu) is estimated from an empirically baseddelay/degree of saturation curve. Delay increases significantly with the total flow,simultaneously with a major and minor flow and with the degree of saturation. Investigationsfrom the manual showed that there is no gap acceptance behavior at high flows. This meansthat western models for stop/give–way behavior of the traffic from the minor road are notapplicable. The maximum stable outflow at predefined conditions is very difficult to define,since the variance in behavior and outflow is enormous. Instead, the capacity has been definedas the total intersection flow when the average delay per vehicle exceeds a pre–defined valueconsidered high, e.g. 15 seconds. Delay values from this method can be used together with


156

delay and travel time values from methods for other types of traffic facilities described in themanual in order to estimate travel times along routes in networks.

Figure 6-6. Relationship between Delay and Degree of Saturation (IHCM, 1997)

In this study, delay (sec/pcu) was defined as an average delay per entering vehicle and it isestimated from the empirical relationship between delay and degree of saturation. Totalaverage delay of vehicles at intersection is calculated by

DSDDSif

⋅+=≤

2078.82,60.0

and

( )DSD

DSif

⋅−=

>

2042.02742.00504.1,60.0

whereDS = Degree of Saturation [-]D = Delay [sec/pcu]

(see also Figure 6-6).

(6-5)

(6-6)


157

This formula was presented in the INDONESIAN HIGHWAY CAPACITY MANUAL(No. 09/T/BNKT/1993), which is known as the preliminary research report from the Ministryof Public Works after two years observation at 275 locations in 16 cities in Indonesia.However, this only considers the delay as a total average of vehicles’ delay at intersectionwithout analyzing any impact on each traffic stream or traffic flow from major and minorroads. Further improvements on delay measurement have been presented in the final reportwhich is the INDONESIAN HIGHWAY CAPACITY MANUAL (1997) where delay wasfound to be smaller (1 – DS)·2 than in the previous approach, see Figure 6-7, Equations 6-7and Equation 6-8.

Figure 6-7. Delay at Intersection (DT1) and Degree of Saturation (DS) (IHCM, 1997)

The graph above shows the relationship between the average delay of vehicles (DT1) and thedegree of saturation (DS) at intersections. The corresponding formulas are shown below.

[ ]

[ ] [ ]212042.02742.0

0504.1,6.0

2120788260

⋅−−⋅−

=

>

⋅−−⋅+=≤

DS)(DS)(

D

DSif

DS)(DS).(D,.DSif

It is very clear that traffic flow could only find delay no more than 15 seconds per passengercar unit even if the capacity has already been reached (DS=1.00). It is still in level of serviceB (LOS B) if we refer to the HCM (2000). It was a fact that an unsignalized intersection under

0

5

10

15

20

25

30

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1,2Derajat Kejenuhan (DS)

Tun

daan

Tot

al L

alul

inta

s D

T 1 (

det/

smp)

DT = 2 + 8,2078*DS - (1-DS)*2 untuk DS ≤ 0,6

DT = 1,0504 / (0,2742 - 0,2042*DS) - (1 - DS)*2 untuk DS > 0,6

(6-7)

(6-8)


158

such conditions could still produce high flow even at the high degree of saturation becausevehicles do not have to wait too long in order to cross the intersection (small delay).

Instead, the common rules of priority (priority to the major road) and the rules of ”nearsidepriority” three–leg unsignalized intersections where traffic approaching on the minor roadgives way to major through and right–turning traffic, delays are experienced only by traffic inthe left and right–turning minor road streams where delays depend on the flow demand inthese streams and the capacity available to them, both of which will vary in time (KIMBERet al., 1977). However, it has been noted that frequently drivers’ understanding of the rule ispoor and because of that, in some cases the intersections seem not to be working correctlyfrom the priority rule point of view (SECO, 1991) which is a very common situation inIndonesia.

The study from the INDONESIAN HIGHWAY CAPACITY MANUAL (1997) found thatdrivers were more aggressive and risky when the degree of saturation was higher than 0.8 –0.9 while drivers are scrambling limited space in conflict areas. The following method fromthe manual has considered such a condition and suggested not to use the method since drivers’behavior was changed because of such rules of stop and give–ways or priority–to–the–leftexist that the method was no longer suitable. Therefore, the study should have been taken intoconsideration of the delays that might have occurred or almost always occur at all legs, forvehicles at the major road and vehicles at the minor road.

Figure 6-8. Delay at Major Road (DTMA) and Degree of Saturation (DS) (IHCM, 1997)

0

5

10

15

20

25

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1,2Derajat Kejenuhan DS

Tund

aan

Uta

ma

Lalu

linta

s D

TM

A(d

et/s

mp) DT = 1,8 + 5,8234*DS - (1 - DS)*1,8 untuk DS ≤ 0,6

DT = 1,05034 / (0,346 - 0,246*DS) - (1 - DS)*1,8 untuk DS > 0,6


159

To calculate possibilities of delay at the legs of the intersection, the manual has presented amethod of delay analysis at the legs (major and minor roads). Delay at major roads, DMA canbe calculated as (see also Figure 6-8)

[ ]

[ ] [ ]8.11246.0346.0

05034.16.0

8.118234.58.16.0

⋅−−⋅−

=

>

⋅−−⋅+=≤

DS)(DS)(

D

:DSif

DS)(DS)(D:DSif

MA

MA

and delay at minor road, DMI is measured as

[ ]MI

MAMATOTALMI Q

)D(Q)DT(QD ⋅−⋅= 1

where

DS = Degree of Saturation [-]DMA = Delay at major road [sec/pcu]DMI = Delay at minor road [sec/pcu]QTOTAL = Total traffic flow [pcu/h]DT1 = Delay at intersection [sec/pcu]

However, there are several sources which resulted in delay at unsignalized intersections. First,vehicles have to slow down negotiating the intersection because they have to respond to thesystem implemented and be ready to give way to priority traffic. They may have to queuebefore they can enter the intersection (KIMBER et al., 1986). And two main components havebeen separated conceptually; ”geometric delay” – the intrinsic delay arising from the need toslow down, negotiate the intersection and accelerate back to running speed – and congestiondelay. The first is defined for single isolated vehicles, and the second arises from vehicle –vehicle interactions.

The geometric delay, DG is one of the parameters related to the average geometric delay of allvehicles (motorized) involved/crossing the intersection due to the geometric design of theintersection. The geometric delay is the delay that a vehicle would incur if it passed throughthe intersection in complete isolation, and if the driver knew he was travelling in isolation(KIMBER, SUMMERSGILL, BURROW, 1986). This delay represents the differencebetween two journey times: the journey time, that the driver would experience between twoarbitrary points, upstream and downstream of the intersection and remote from its influence,and the ”reference” journey time on such idealized comparable linkages providing anequivalent connection. The delays following from these definitions exclude the effects of

(6-9)

(6-10)

(6-11)


160

queuing for entry to the intersection and those caused by drivers’ reducing speed in order tocheck whether they will safely be able to enter the junction immediately on arrival. Geometricdelays (DG) were calculated as follows

[ ]

4:1

43161:1

=≥

⋅+⋅−+⋅⋅−=<

DGDSif

)(DS)p()(pDS)(DGDSif

TT

where

DS = Degree of Saturation [-]DG = Geometric delay [sec/pcu]pT = Ratio of turning–flow [-]

Due to the influence of geometric design of intersection, the total delay, D of vehicles at anintersection is measured as

DGDTD += 1

where

D = Total delay of the intersection [sec/pcu]DT1 = Average delay of the intersection according to eq. 6-7 and 6-8 [sec/pcu]DG = Geometric delay [sec/pcu]

From the measured flow data and the calculated maximum flow of intersections, thefollowing graph (Figure 6-9) is plotted based on Equation 6-14. Assessments to the model arebased on maximum flow which was reached at a certain speed (v=11 km/h, v=12 km/h). Thisspeed level produced capacities from the new method which are is very close to the capacitycalculated by the manual and data (cf. Figure 6-1). We can see from the figure that at thislevel, delay that was found from approaches remain to have almost the same value. Themaximum delay is less than 12 seconds per passenger car unit (e.g. intersection–1 andintersection–5) and the minimum delay is about 6 seconds per passenger car unit (e.gintersection–4, intersection–8 and intersection–9). It can be concluded that the intersectionsoperate still in an appropriate/good performance.

(6-12)

(6-13)

(6-14)


161

Figure 6-9. Calibrated Delay Data – IHCM – Model (v = 11 km/h and v = 12 km/h)

Because the degree of saturation, DS is higher than 0.60 only at intersection–1 andintersection–5, the calculated delay are a bit higher at both intersections with more than 10seconds per passenger car unit. Unfortunately, the Indonesian manual does not mention anyparameters/values to measure the quality performance of unsignalized intersections instead ofadoption of quality measurement from the HIGHWAY CAPACITY MANUAL (1994). Ifwe refer to the HIGHWAY CAPACITY MANUAL (2000), both intersections remain atlevel of service B (LOS B) while others are at level of service A (LOS A), see Table 6-13.

In order to make a suitable relationship between delay and degree of saturation forintersections based on actual measurements, some empirical regressions are developed. Twodifferent graphs were made for the degree of saturation smaller than 0.60 due to most of theintersections performed and the other graph was made for a degree of saturation smaller than0.90, because two other intersections could have reached such a saturation (0.69 – 0.71).Approaches were made for two kinds of regression lines, linear and exponential (see Figure 6-10 and Figure 6-11 for DS < 0.60, Figure 6-12 and Figure 6-13 for DS < 0.90).Furthermore, comparison between delays from data (calculated) and assessments from models(IHCM, Model v=11 km/h and Model v=12 km/h) are shown in Figure 6-14 and from that,we can see the delay comparisons retaining differences at a corridor of about 2 seconds lowerand higher.

DELAY

0123456789

101112131415

1 2 3 4 5 6 7 8 9 10

Intersection

Del

ay [

seco

nd/p

cu]

DATAIHCMMODEL V=11 km/hMODEL V=12 km/h


162

Figure 6-10. Linear Regression, DS < 0.60 Figure 6-11. Exponential Delay, DS < 0.60

Figure 6-12. Linear Regression, DS < 0.90 Figure 6-13. Exponential Delay, DS < 0.90

Figure 6-14. Comparison Calculated Delay – IHCM – Model (v = 11 km/h and v = 12 km/h)

DELAY

0

2

4

6

8

10

12

0 0,1 0,2 0,3 0,4 0,5 0,6

Degree of Saturation, DS

Del

ay (s

ec/p

cu)

DS < 0.60

DELAY

0

2

4

6

8

10

12

0 0,1 0,2 0,3 0,4 0,5 0,6


Del

ay (s

ec/p

cu)

DS < 0.60

DELAY

0

2

4

6

8

10

12

14

16

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8


Del

ay (

sec/

pcu)

DS < 0.90

DELAY

0

2

4

6

8

10

12

14

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8


Del

ay (

sec/

pcu)

DS < 0.90

076.5676.7 += DSD DSeD 0376.1348.5=

7995.49326.8 += DSDDSeD 1368.1223.5=

COMPARISON OF CALCULATED DELAY

0

2

4

6

8

10

12

14

16

18

0 2 4 6 8 10 12 14 16 18

Delay (data) [second/pcu]

Cal

cula

ted

[sec

ond/

pcu]

IHCM

MODEL V = 11 km/h

MODEL V = 12 km/h


163

6.6.2 Probability of Queue

There are many problems in traffic engineering under mixed traffic that require methods forthe prediction of queue lengths and vehicle delays at intersections. Some studies havediscussed the application of probabilistic queuing theory to traffic at intersections and showedhow the effects of time–varying demand and capacity could be treated theoretically. Theresults were used to develop a practical flow – delay relationship for major/minor roadintersections. This relationship has proved valuable: It avoids difficulties associated withsteady–state queuing relationships, which predict infinite delays when demand reachescapacity.

The theoretical behavior of queues at major/minor priority intersections under conditions ofstatistical equilibrium has been well documented. Although techniques have also beendeveloped to describe the behavior of queuing systems under condition of variable demandand capacity, they are not used in practice for intersection under mixed traffic where rule ofpriority no longer exist. The simplest technique is to assume that queues grow at a ratedetermined only by the excess of demand over capacity, and decay when demand has fallenbelow capacity, at a rate given by the difference. However, the effects of random fluctuationsin traffic arrivals and departures at the intersections are then ignored, although, in fact, theyare extremely important. For example, in cases where the peak demand does not quite reachcapacity the predicted delays are zero, whereas in reality they are known to be considerable.

Probability of queue is defined as the probability of more than two vehicles in queue at everyapproach of unsignalized intersections (IHCM, 1997). There is thus a need for realisticprocedures for queue prediction that take into account demand and capacity (maximum flow)of intersections. A range of queue probability QP% (%) is estimated from the empiricalrelationship between queue probability QP% and degree of saturation DS which is describedin the Indonesian manual as

queue probability at upper range,32 49.1068.2471.47% DSDSDSQP +−=

and queue probability at lower range is32 49.1066.2002.9% DSDSDSQP ++=

where

QP% = Queue Probability [%]DS = Degree of Saturation [-]

(see also Figure 6-15).

(6-15)

(6-16)


164

Figure 6-15. Range of Queue Probability QP% (%) versus Degree of Saturation, DS (IHCM)

Figure 6-16. Queue Probability QP% at Lower Range (Data – IHCM – Model)

QUEUE PROBABILITY QP%(Lower Range)

0,0

2,5

5,0

7,5

10,0

12,5

15,0

17,5

20,0

22,5

25,0

1 2 3 4 5 6 7 8 9 10

Intersection

Que

ue P

roba

bilit

y (%

)



165

From the figure above, flow measurement and maximum flow (capacity) of each intersection,and the range (%) of possible queues occurring at intersections can be obtained (Figure 6-16for lower range queue probability, Figure 6-17 for higher range queue probability andTable 6-12).

Figure 6-17. Queue Probability QP% at Upper Range (Data – IHCM – Model)

IntersectionAnalysis Input Data

1 2 3 4 5 6 7 8 9 10DATA 0.69 0.57 0.28 0.24 0.51 0.41 0.35 0.16 0.16 0.29IHCM 0.68 0.55 0.30 0.25 0.54 0.42 0.36 0.13 0.17 0.29MODELV=11 km/h 0.49 0.34 0.34 0.13 0.57 0.29 0.39 0.14 0.10 0.28

Degree ofSaturation

DS MODELV=12 km/h 0.59 0.42 0.42 0.16 0.71 0.36 0.48 0.16 0.13 0.33

DATA 19.41 13.99 4.39 3.49 11.44 7.85 6.01 1.94 2.02 4.64IHCM 18.93 12.88 4.87 3.74 12.43 8.10 6.49 1.53 2.23 4.56MODELV=11 km/h 10.45 5.96 5.95 1.60 13.93 4.70 7.32 1.66 1.17 4.30

LowerRangeQP%(%) MODEL

V=12 km/h 14.79 8.17 8.32 2.05 20.44 6.37 10.10 2.10 1.50 5.61

DATA 39.56 29.97 12.70 10.80 25.54 19.22 15.86 7.03 7.25 13.20IHCM 38.68 28.04 13.65 11.34 27.26 19.66 16.75 5.87 7.79 13.05MODELV=11 km/h 23.81 15.77 15.74 6.07 29.86 13.32 18.27 6.25 4.76 12.50

UpperRangeQP%(%) MODEL

V=12 km/h 31.36 19.79 20.07 7.34 41.41 16.53 23.19 7.45 5.78 15.10

Table 6-12. Range of Queue Probability QP% [%] Calculation with Degree of Saturation, DS

QUEUE PROBABILITY QP%(Upper Range)

0,0

5,0

10,0

15,0

20,0

25,0

30,0

35,0

40,0

45,0

50,0

1 2 3 4 5 6 7 8 9 10

Intersection

Que

ue P

roba

bilit

y (%

)



166

Figure 6-18. Graph for the Range of Queue Probability QP% of Intersections

Assessment data of flow measurement and maximum flow intersections were then used todevelop an appropriate range model of queue probability for intersections. Calculated queueprobability from the manual with speed ranges between 11.0 km/h – 12.0 km/h wouldproduce regression lines,

for upper range QP%,

999.02307.0974.45867.19096.52%

2

23

=

++−=

RDSDSDSQP

and for lower range QP%,

999.00863.03029.8872.223395.8%

2

23

=

+++=

RDSDSDSQP

where

QP% = Queue probability [%]DS = Degree of Saturation [-]

The regression lines are similar to the formula from the manual (Figure 6-15) as can be seenin Figure 6-18. There is an additional constant of 0.2307 for a upper range QP% and 0.0863for a lower QP%.

(6-17)

(6-18)

QUEUE PROBABILITY

0

5

10

15

20

25

30

35

40

45

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1


Que

ue P

roba

bilit

y, Q

P%

Lower range QP%Upper range QP%

2307.0974.45867.19096.52% 23 ++−= DSDSDSQP

0863.03029.8872.223395.8% 23 ++−= DSDSDSQP


167

6.7 Conclusions

Results of parameters related to maximum flow (capacity) and other parameters of traffic flowquality and performance, e.g. delay and queue probability have been calculated for allintersections. It can be concluded that the maximum flows were reached at the average speedof vehicles within the range of 11 km/h – 12 km/h which is appropriate for all intersections.This corresponds to the results from the manual. Based on the measurements and themaximum flows analyzed by the new model, it was found that the degree of saturation (DS) ofintersections has less than 0.35 in average and the percentage area of intersection occupiedhas less than 11%.

IntersectionDegree ofSaturation

(DS)Delay

[sec/pcu] QP% lower QP% upperIntersectionOccupancy

[%]

Level ofService(LOS)

1 0.59 10.2 14.79 31.36 11.02 B2 0.42 8.6 8.17 19.79 10.44 A3 0.42 8.1 8.32 20.07 10.14 A4 0.16 6.2 2.05 7.34 10.06 A5 0.71 11.2 20.44 41.41 9.77 B6 0.36 7.9 6.37 16.53 10.88 A7 0.48 9.1 10.10 23.19 10.39 A8 0.16 6.6 2.10 7.45 10.99 A9 0.13 5.6 1.50 5.78 9.72 A

10 0.33 8.3 5.61 15.10 10.44 ATable 6-13. Traffic Quality of Intersections

Referring to the final report of KALIMANTAN URBAN DEVELOPMENT PROJECT(1998), the quality/level of service at unsignalized intersections is measured based on theprobability of queues and delays of each stream and the level of service is classified as six (6)levels; A, B, C, D, E and F (HIGHWAY CAPACITY MANUAL, 1994). When we adopt thelevel performance criteria from HIGHWAY CAPACITY MANUAL (2000) whichcorrespond to the delay, the intersections would remain at level A and only intersection–1and intersection–5 remain at level B (see also Table 6-11). The following Table 6-13performed details on traffic flow quality at the speed v=12 km/h.

7 Pedestrians’ Behavior

168


Walking is the predominant transport mode for short trips in Indonesian cities. A study hasshown (SOEGIJOKO, 1991) that 40 percent of all household trips were walking trips thatconstituted 50 percent of all travel time and 32 percent of all traveled distances. Similarresults from a number of other studies indicated that walking was the most important urbantransport mode, from the point of view of economic and basic needs.

In Indonesian cities, pedestrian problems essentially break down into the followingcategories :■ At the village community level, the lack of paved surfaces and poor drainage constitute a

major lack of amenities in the rainy season.■ In areas where motorized traffic predominates, the lack of continuous networks of

footpaths, sidewalks, and adequately enforced pedestrian priority crossings forcespedestrians to compete with motorized traffic for use of the road space.

■ At all levels, there is a lack of an adequate standard of design of pedestrian facilities.

Pedestrians are very dominant in the part of the cities close to the central business andactivities, e.g. market, offices, schools and station. At the certain condition this might nothave any impact to other modes of transport, but when pedestrians are on the streets andmixed with other modes while they have an equal right to use roads as the drivers, therewould be impacts for the quality of road traffic, because pedestrians have typical dynamiccharacteristics as

- Speed limit = 5 km/h- Average speed = 3.5 km/h- Ideal trip length = 400 m- Average trip length = 1.1 km

The study has not considered the aspects of pedestrians crossing intersections because mostintersections were not facilitated for pedestrians crossing, or there were no adequatepedestrians crossing at intersections (e.g. sidewalks). Another problem was that pedestrianstend to cross the intersection everywhere they want since they have enough time (”gap”) andfeel safe to cross. WIDJAJANTI (2001) has observed the behavior of pedestrians crossing atintersections in Indonesia and found out that there were significant aspects between ”gap” ofpedestrians and the speed of traffic streams/vehicles. The gap of pedestrians is defined as thetime between 2 consecutive vehicles that was required by pedestrians to cross the intersection.

At unsignalized intersections, most of the pedestrians took the first opportunity to cross theroad regardless to the fact of whether the coming drivers have noticed their existence or not.


169

This might be due to the fact that the drivers at unsignalized intersections rarely considergiving the pedestrians a chance to cross. Most of the drivers’ compliance at unsignalizedintersections was due to the slow moving traffic. Lack of education might be another reasonwhy, both, drivers and pedestrians revealed unaccepted behavior, especially at unsignalizedintersections.

Based on the field investigation and data measurement, it was found that, in general, theaverage speed of pedestrians crossing the road close to the conflict area of intersections isbetween 3.0 km/h – 4.0 km/h and the number of pedestrians walking is higher thanpedestrians crossing the intersection in most cases, see also Table 7-1 below.

Pedestrian

CrossingIntersection Traffic Flow[veh/h]

Number Average Speed [km/h]Walking

1 4626 23 3.69 422 4928 43 4.46 3613 4902 116 3.17 3344 3724 33 2.43 815 7240 3 4.10 156 5173 50 5.10 687 3734 67 3.88 2188 2358 47 2.60 859 2453 28 3.68 52

10 2158 18 2.88 19Table 7-1. Number of Pedestrians Crossing and Walking at the Unsignalized Intersections

It was rather difficult to give a conclusion concerning the relationship between the number ofvehicles and the speed of pedestrians crossing, because it was found that at a large number ofvehicles, the speed of pedestrians crossing is relatively high because they fell safe to cross theroad as soon as possible, but in another case, the pedestrians have to stop at the certain pointof road (one or several times) to give way to vehicles before they completed crossing the road.

Figure 7-1. Pedestrian Crossing with 2 (two) Figure 7-1. Pedestrian Crossing with 4 (four) Phases Phases

Pedestrians crossing

Crossing path

Vehicles stream

[1]

[2]Pedestrians phase

Pedestrians crossing

[1]

[2]

[3]

[4]

Vehicles stream

Crossing path

Pedestrians phase


170

In this case, they required to have more time in order to cross the road. This phenomenon isdescribed in Figure 7-1 (pedestrian crossing with 2 phases/direct crossing) and Figure 7-2(pedestrian crossing with 4 phases). It was assumed that vehicle streams have the same speedin both directions in both cases. From Figure 7-1 we can see that pedestrians would only have2 phases (direct crossing) in order to cross the road as long as they feel safe to do so withconsidering the speed of vehicles and their own speed (time needed) to complete the crossing.However, Figure 7-2 shows that the pedestrians prefer to take 4 phases to complete thecrossing even the speed of vehicles remain the same as in the previous case. It is clear that thesecond case would take more time.

During the field investigation, lack of pedestrians facilities were found at the intersections.For example, there was no zebra crossing and side walks but instead there were road shoulderwhich were used for pedestrians walking. However, in most of the cases in Indonesiapedestrians did not use zebra crossing and the drivers did not give an opportunity to them tocross the road even though the pedestrians should have a priority (MALKHAMAH, 2000).

JANAHI & MADANI (1998) have observed pedestrians’ behavior at signalized andunsignalized intersections. They found that the rate of the compliance of both drivers andvehicles did not show any significant difference through out the day. It was thought that therate of compliance would improve during night compared to other times of the day based onthe fact that drivers and pedestrians, alike, are commuting social or leisure trips where theywill be in less hurry. It was thought that the speed of pedestrians at unsignalized intersectionswould be higher than at signalized intersections where they might feel more secure whilepassing. The results revealed that there was no significant difference between the two groups,even though the speed of the pedestrians at the unsignalized intersections was higher.

Observations in big cities, e.g. Jakarta (WIDJAJANTI, 1999) found that speed of pedestriansis relative higher in big cities with a common speed of 77 m/minute – 79 m/minute. 50% ofthe pedestrians have accepted an average ”gap” of 36.67 meter while the average speed ofvehicles was 29.79 km/h. Results showed that for an increasing speed of vehicles of every5 km/h, the ”gap” would be increased by 7 meters and there was a significant relationshipbetween the ”gap” required by pedestrians and the speed of vehicles at intersections.

Due to the phenomenon above, this study has not considered the pedestrians as one of theconflict streams since they tend to cross the intersection with an appropriate ”gap”, such thatthey feel safe and because there was lack of standard facilities for pedestrians, they couldcross intersections at every point (of roads) they wanted to. Instead of that, the currentapproach of capacity calculation in the Indonesian manual has considered and measured thepedestrians as a ”side friction” which is also included in this study and analysis.

8 Conclusions and Recommendations

171


Studies have been performed on capacity calculation at unsignalized intersections by severalways and based on several rules, e.g. priority, gap acceptance, and empirical approach.Models from developed countries mainly use the gap acceptance method while the UnitedKingdom created its model by empirical regression based on a large number of field data inmodern British streets. Recent advance in capacity analysis at unsignalized intersections wasdeveloped by the conflict technique. The method considered the interactions between streamsin a way that each stream would have equal hierarchy in departure and arrival at theintersections. Therefore, the procedure is applied in such a way that the First–In–First–Out(FIFO) discipline is applied and intersections with this discipline are common in developingcountries.

However, most of the methods rely on rules and discipline (patterns) which do not exist inmost of the developing countries. Instead of gap acceptance and priority, vehicles have toenter intersections alternatively/one after another (FIFO). Since there is no priority, anddiscipline, the priority models can not be applied appropriately. Therefore, most of themethods from developed countries were not suitable for developing countries because therewere lack of lane discipline and no gap behavior. E.g. accepted gaps are less than 2 secondsand the traffic is more heterogeneous which consists of a large number of vehicle types. Thetypical traffic behavior in developing countries is that of non–motorized transport which issmaller in static and dynamic dimensions are not attended. In such a case, it is necessary todevelop a new method to calculate the capacity with considering the interactions betweenstreams based on the relationship between speed and flow of streams.

The study has investigated the possibility of correlation between speed, flow and intersectionoccupancy at three–leg unsignalized intersections in Indonesia. Each stream flow (sixstreams) was observed corresponding to its speed, flow, and percentage of intersection areaoccupied by vehicles. This approach has defined the streams as six (6) groups of conflictstreams where each group would have two to four streams. Speed and flow were counted in1–minute and 5–minute intervals while occupancy was counted in 20–second intervals withthe total time of observation of one hour for each intersection. From data recorded,relationships between the three parameters were developed, e.g. the speed and flowrelationship and the flow and intersection occupancy relationship. The results showed thatthere was a good correlation between speed – flow and flow – intersection occupancy in eachgroup of conflict.

Furthermore, the model of capacity which is defined as the maximum possible flow of theintersection was developed corresponding to the relationship of speed and flow of streams ateach group of conflict. Based on traffic flow measurements and speed prediction at the


172

maximum flow of a stream, the total capacity of an intersection can be calculated and themaximum flow of each stream can also be measured. Since there were no data of speed at themaximum flow (capacity) at intersections, the study has made an assumption for the speedwhich also refers to the secondary data (previous study). All possible maximum flows atintersections have been calculated by using the model (equation) and use the matrix forsimplification. The real maximum flow as the capacity of the intersection is the least of allpossible maximum flows.

This study has also calculated the capacity based on the INDONESIAN HIGHWAYCAPACITY MANUAL (IHCM, 1997) which is required to compare the results from the newapproach. It has been demonstrated that there were small differences in capacity between bothmethods (IHCM and the new approach) at the speed range of 11 km/h and 12 km/h. It can beconcluded that the new approach could be suitable to calculate the capacity of unsignalizedintersections under mixed traffic flow, especially for Indonesia as an alternative instead of themethod by the INDONESIAN HIGHWAY CAPACITY MANUAL (1997).

Since this study has not observed the real delay at intersections due to a small number ofvehicle stops (less than 0.5%), this study estimated the quality of traffic flow at unsignalizedintersections based on the delay and probability of queue calculated from the Indonesianmanual corresponding to the level of service (LOS) from the HIGHWAY CAPACITYMANUAL (HCM, 2000). The study recommends to take the data observations in more thanone hour until the maximum flow of intersections is reached and the real speed can becounted in order to achieve a better prediction. It is also required to make observations inmore cities to improve the representative character of the new method.

9 References

173

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APPENDIX A : Geometric Design and Traffic Flow Performance

A - 1

Appendix A : Geometric Design and Traffic Flow Performance

A – 1. Intersection : Komodor Yos Sudarso – Hasanuddin – Pak Kasih

A – 1. 1 Geometric Design of Intersection


A - 2

A – 1. 2 Traffic Flow and Environment of Intersection

Type of Intersection Three-legs/324A B CNumber of Lanes 4 2 4

Road Entry Widths [m] 16.4 10.7 9.0Median No median at major road

Road Environment Commercial and residential landuse with direct road side access forpedestrians and vehicles

Side Friction Commercial medium (300 – 499 activities per hour)Population 0.3 – 1.0 million inhabitantsTime of Measurement 14.20 – 15.20 , 15.35 – 16.35Number ofUnmotorized 83 (1.8%)

Number of Pedestrian 65 (1.4%)

1206480

480571

1300748

AC

B

12

3 4

56

[veh/h]

Schematic of Traffic Stream


A - 3

A – 1. 3 Flow, Speed and Intersection Occupancy of Each Stream

5957555351494745434139373533312927252321191715131197531

1-minute interval

120

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]

Intersection Occupancy [ % ]


A - 4

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

43,5

31,0

25,1

21,6

19,3

17,2

15,6

14,1

12,6

11,09,4

7,5

2,3

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow

Speed of Each Stream [ Km/h ]

Cumulative Speed [ % ]


A - 5

A – 1. 4 Traffic Flow of Each Stream


Direction of flow

1.600

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

748

1.300

571

480

321

1.207

Flow of each stream


Direction of flow

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flow

(pcu

/h)

350,2

639,2

272,2

222,3

168,2

570,3

Flow of each stream

26,5%

19,3%

54,2%

left-turn flowright-turn flowstraight flow

Direction of flowDirection of Flow [ % ]


A - 6

A – 1. 5 Traffic Flow and Speed of Each Vehicle

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

4.000

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

12458

3.483

819

10

212

20


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1000,0

900,0

800,0

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flow

(pcu

/h)

9,016,817,4

696,6

819,0

16,0

593,6

54,0


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

Speed Performance [ Km/h ]


A - 7


Traffic Performance of Conflict Group–I


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flow

(veh

/h)

B - AC - A

Direction of flow

RickshawBicycleMotorcycleCarMinibusTruck 2 …Truck 3 …

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Perc

enta

ge

B - AC - A

Direction of flow


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 8

Traffic Performance of Conflict Group–II

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flow

(veh

/h)


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Perc

enta

ge


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 9

Traffic Performance of Conflict Group–III


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flow

(veh

/h)

A - CB - C

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Perc

enta

ge

A - CB - C

Direction of flow


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 10

Traffic Performance of Conflict Group–IV


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 11

Traffic Performance of Conflict Group–V


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 12

Traffic Performance of Conflict Group–VI

Pushcart 2 wheels


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 13

A – 2. Intersection : K.H. Wahid Hasyim – Hasanuddin



A - 14

A – 2. 2 Flow and Environment of Intersection




Side Friction Commercial medium (300 – 499 activities per hour)Population 1.0 – 3.0 million inhabitantsTime of Measurement 09.00 – 10.00, 11.00 – 12.00Number ofUnmotorized 177 (3.6%)


1097681

795549

1238569

AC

B

12

3 4

56

[veh/h]



A - 15

A - 2. 3 Flow, Speed and Intersection Occupancy of Each Stream

5957555351494745434139373533312927252321191715131197531

1-minute interval

120

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]



A - 16

5957555351494745434139373533312927252321191715131197531

1-minute interval

50,0

47,5

45,0

42,5

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

42,8

34,5

30,9

27,7

25,0

22,4

20,3

18,3

16,3

14,3

12,3

10,38,3

6,3

3,5

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow

Speed of Each Stream [Km/h]

Cumulative Speed [%]


A - 17

A - 2. 4 Traffic Flow of Each Stream


Direction of flow

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

569

1.238

549

795

681

1.097

Flow of each stream


Direction of flow

500,0

400,0

300,0

200,0

100,0

0,0

Flo

w (

pcu

/h)

237,7

387,1

244,4

199,1203,1

362,4

Flow of each stream

27,7%

25,0%

47,4%


Direction fo flowDirection of Flow [%]


A - 18


Pushcart 2 wheels

TricyclesRickshawBicycleMotorcycleCarMinibusTruck 2 axles

Truck 3 axles

Type of vehicle

6.000

5.500

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

1436136

4.130

544

2751


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1000,0

900,0

800,0

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flo

w (

pcu

/h)

1,02,021,627,2

826,0

544,0

4,2

202,5

5,3


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

Speed Performance [Km/h]


A - 19



Pushcart 2 wheels

TricyclesRickshawBicycleMotorcycleCarTruck 2 axles

Truck 3 axles

Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 20



Truck 3 axles

Type of vehicle

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 21



Type of vehicle

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow

TricyclesRickshawBicycleMotorcycleCarMinibusTruck 2 …

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Perc

enta

ge

A - CB - C

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 22


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 23



Truck 3 axles

Type of vehicle

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Truck 3 axles

Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 24



Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 25

A – 3. Intersection : Komodor Yos Sudarso – Tebu



A - 26







2381701

446115

1141118

AC

B

12

3 4

56

[veh/h]



A - 27


5957555351494745434139373533312927252321191715131197531

1-minute interval

140

130

120

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

21,0

20,0

19,0

18,0

17,0

16,0

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]



A - 28

5957555351494745434139373533312927252321191715131197531

1-minute interval

60,0

57,5

55,0

52,5

50,0

47,5

45,0

42,5

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

55,2

36,8

31,9

28,5

26,8

25,2

23,6

22,2

20,3

18,8

17,5

16,4

15,4

14,3

13,3

12,3

11,3

10,39,3

8,3

7,3

6,3

5,3

4,3

3,3

1,4

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow

Speed of Each Stream [Km/h]

Cumulative Speed [Km/h]


A - 29



Direction of flow

2.600

2.400

2.200

2.000

1.800

1.600

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

118

1.141

115

446

701

2.381

Flow of each stream


Direction of flow

700,00

600,00

500,00

400,00

300,00

200,00

100,00

0,00

Flo

w (

pcu

/h)

16,60

273,10

24,30

63,1084,60

558,80

Flow of each stream

11,5%

16,6%

71,8%


Direction of flowDirection of Flow [%]


A - 30


Pushcart 2 wheels


Type of vehicle

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

3210199

4.262

357

2148


Pushcart 2 wheels


Type of vehicle

700,00

600,00

500,00

400,00

300,00

200,00

100,00

0,00

Flo

w (

pcu

/h)

2,702,005,00

39,80

426,20

357,00

29,40

158,40


Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 31



Pushcart 2 wheels


Type of vehicle

2.200

2.100

2.000

1.900

1.800

1.700

1.600

1.500

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 32



Type of vehicle

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 33



Type of vehicle

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow


Type of vehicle

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 34


Pushcart 2 wheels


Type of vehicle

2.200

2.100

2.000

1.900

1.800

1.700

1.600

1.500

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 35



Type of vehicle

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 36


RickshawBicycleMotorcycleCar

Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 37

A – 4. Intersection : Tanjung Raya – Panglima Aim



A - 38




Road Environment Public service and residential landuse with direct road side access forpedestrians and vehicles

Side Friction Residential medium (300 – 499 activities per hour)Population 0.3 – 1.0 million inhabitantsTime of Measurement 14.35 – 15.35, 15.45 – 16.45Number ofUnmotorized 169 (4.5%)


712210

248681

986887

AC

B12

3 4

56

[veh/h]



A - 39


5957555351494745434139373533312927252321191715131197531

1-minute interval

120

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

21,0

20,0

19,0

18,0

17,0

16,0

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]



A - 40

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

36,5

33,1

30,4

27,8

25,9

24,3

22,2

20,7

19,5

18,3

17,0

16,0

14,9

13,9

12,9

11,9

10,99,9

8,9

7,9

6,9

5,9

4,9

3,9

2,9

1,8

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow




A - 41



Direction of flow

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

887

986

681

248210

712

Flow of each stream


Direction of flow

300,00

250,00

200,00

150,00

100,00

50,00

0,00

Flo

w (

pcu

/h)

169,60180,10

138,80

33,6034,20

123,60

Flow of each stream

30,5%

23,9%

45,6%




A - 42


Pushcart 2 wheels


Type of vehicle

4.000

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

431161

3.285

240

129


Pushcart 2 wheels


Type of vehicle

400,00

350,00

300,00

250,00

200,00

150,00

100,00

50,00

0,00

Flo

w (

pcu

/h)

3,200,900,70

32,20

328,50

240,00

1,90

72,50


Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 43



Pushcart 2 wheels

TricyclesBicycleMotorcycleCarMinibusTruck 2 axles

Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 44


Pushcart 2 wheels


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 45


Pushcart 2 wheels

TricyclesBicycleMotorcycleCarTruck 2 axles

Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 46


Pushcart 2 wheels


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 47


Pushcart 2 wheels


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 48


Pushcart 2 wheels


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 49

A – 5. Intersection : Sultan Abdurrahman – Putri Candramidi



A - 50







25451030

986219

2220240

AC

B12

3 4

56

[veh/h]



A - 51


5957555351494745434139373533312927252321191715131197531

1-minute interval

170

160

150

140

130

120

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

21,0

20,0

19,0

18,0

17,0

16,0

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]



A - 52

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

59,7

39,4

34,7

32,0

29,2

26,8

25,2

23,8

22,5

21,2

20,1

19,1

17,8

16,6

15,6

14,6

13,6

12,6

11,6

10,69,6

8,6

7,6

6,6

5,6

4,6

3,6

2,5,8

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow




A - 53



Direction of flow

3.000

2.800

2.600

2.400

2.200

2.000

1.800

1.600

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

240

2.220

219

9861.030

2.545

Flow of each stream


Direction of flow

700,00

600,00

500,00

400,00

300,00

200,00

100,00

0,00

Flo

w (

pcu

/h)

35,30

562,50

37,80

231,40216,70

617,80

Flow of each stream

16,9%

17,3%

65,8%




A - 54


Pushcart 2 wheels


Type of vehicle

8.000

7.500

7.000

6.500

6.000

5.500

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

1319196

6.084

876

1744


Pushcart 2 wheels


Type of vehicle

1000,00

900,00

800,00

700,00

600,00

500,00

400,00

300,00

200,00

100,00

0,00

Flo

w (

pcu

/h)

1,401,5011,4039,20

608,40

876,00

27,20

136,40


Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 55



Pushcart 2 wheels


Type of vehicle

2.200

2.100

2.000

1.900

1.800

1.700

1.600

1.500

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 56



Type of vehicle

2.000

1.900

1.800

1.700

1.600

1.500

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 57



Type of vehicle

2.000

1.900

1.800

1.700

1.600

1.500

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 58


Pushcart 2 wheels


Type of vehicle

2.200

2.100

2.000

1.900

1.800

1.700

1.600

1.500

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 59



Type of vehicle

2.000

1.900

1.800

1.700

1.600

1.500

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 60


RickshawBicycleMotorcycleCarTruck 2 axles

Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 61

A – 6. Intersection : Alianyang – K. H. Wahid Hasyim – K.H. Ahmad Dahlan



A - 62







1429151

203887

1575929

AC

B

12

3 4

56

[veh/h]



A - 63


5957555351494745434139373533312927252321191715131197531

1-minute interval

140

130

120

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]



A - 64

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

27,4

24,1

22,2

20,3

19,1

17,7

16,7

15,6

14,6

13,5

12,5

11,5

10,69,6

8,6

7,6

6,6

5,6

4,6

3,6

2,6

1,5

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow




A - 65



Direction of flow

1.800

1.600

1.400

1.200

1.000

800

600

400

200

0

Flow

(veh

/h)

929

1.575

887

203150

1.429

Flow of each stream


Direction of flow

500,0

400,0

300,0

200,0

100,0

0,0

Flo

w (

pcu

/h)

303,6

367,8

232,6

36,344,2

392,3

Flow of each stream

21,9%

20,0%

58,1%




A - 66


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

5.000

4.500

4.000

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

4.185

2236113

789

6391


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

900,0

800,0

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flo

w (

pcu

/h)

1,80,821,622,6

418,5

789,0

10,8

109,2

2,5


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 67



Pushcart 2 wheels


Type of vehicle

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 68


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 69


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 70


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 71


Pushcart 2 wheels


Truck 3 axles

Type of vehicle

1.400

1.300

1.200

1.100

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Truck 3 axles

Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 72



Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 73

A – 7. Intersection : Hasanuddin – Merdeka


Mark

et

Hosp

ital

Mark

et

Mark

et

Hous

e

Publi

c Se

rvice

1.0

1.0

9.0

1.0

1.0

9.20

1.0

1.0

9.20

Hosp

ital

Cana

l

Cana

l

HASA

NUDD

IN S

TREE

T

MERDEKA STREET

N


A - 74







841828

672261

801331

AC

B

12

3 4

56

[veh/h]



A - 75


5957555351494745434139373533312927252321191715131197531

1-minute interval

110

100

90

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,00

14,00

13,00

12,00

11,00

10,00

9,00

8,00

7,00

6,00

5,00

4,00

3,00

2,00

1,00

0,00

Inte

rsec

tion

occu

panc

y (%

)

Flow [Veh/1-min]



A - 76

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

38,3

29,1

25,5

22,7

21,0

19,4

18,1

16,9

15,9

15,0

14,3

13,7

13,0

12,3

11,6

11,0

10,4

10,09,5

9,0

8,5

8,1

7,6

7,0

6,4

5,7

5,0

2,3

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow




A - 77



Direction of flow

1.000

800

600

400

200

0

Flow

(veh

/h)

331

801

261

672

828841

Flow of each stream


Direction of flow

500,0

400,0

300,0

200,0

100,0

0,0F

low

(p

cu/h

)

84,8

271,2

109,4

170,2

204,6

347,2

Flow of each stream

26,9%

29,2%

44,0%




A - 78


Pushcart 2 wheels


Type of vehicle

3.500

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

159

212

3.030

384

1038


Pushcart 2 wheels


Type of vehicle

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flo

w (

pcu

/h)

1,235,442,4

606,0

384,0

12,0

106,4


Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 79




Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 80


Pushcart 2 wheels


Type of vehicle

1.000

900

800

700

600

500

400

300

200

100

0

Flo

w 8

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 81


Pushcart 2 wheels


Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w 8

veh

/h)

A - CB - C

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 82


Pushcart 2 wheels


Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 83


Pushcart 2 wheels


Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 84



Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 85

A – 8. Intersection : R.E. Martadinata – Tabrani Ahmad


Market

House

Market

House

1.0

1.0

5.80 1.0

1.0

5.0

1.0

1.06.7

0

Hous

e

Hous

e

Hous

e

R. E M

ARTA

DINAT

A ST

REET

TABR

ANI A

HMAD

STR

EET

R. E M

ARTA

DINATA

STR

EET

N


A - 86




Road Environment Residential landuse with direct road side access for pedestrians andvehicles

Side Friction Residential low (100 – 299 activities per hour)Population 0.3 – 1.0 million inhabitantsTime of Measurement 14.45 – 15.45, 15.55 – 16.55Number ofUnmotorized 128 (5.4%)


455443

694179

421166

AC

B

12

3 4

56

[veh/h]



A - 87


5957555351494745434139373533312927252321191715131197531

1-minute interval

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)

Flow [Veh/1-min]



A - 88

5957555351494745434139373533312927252321191715131197531

1-minute interval

60,0

57,5

55,0

52,5

50,0

47,5

45,0

42,5

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

-2,5

Mea

n Sp

eed

(km

/h)


Direction of flow

33,2

26,6

21,6

18,8

16,6

15,0

13,8

12,5

11,4

10,49,6

8,8

7,9

7,1

6,4

5,5

4,5

1,7

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow




A - 89



Direction of flow

800

600

400

200

0

Flow

(veh

/h)

166

421

179

694

443455

Flow of each stream


Direction of flow

200,0

150,0

100,0

50,0

0,0

Flo

w (

pcu

/h)

54,5

63,757,7

119,7

74,368,5

Flow of each stream

36,5%

26,4%

37,2%




A - 90


Pushcart 2 wheels


Type of vehicle

3.000

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

2113112

2.056

157

710


Pushcart 2 wheels


Type of vehicle

300,0

250,0

200,0

150,0

100,0

50,0

0,0

Flo

w (

pcu

/h)

2,40,46,5

22,4

205,6

157,0

16,128,0


Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 91



Pushcart 2 wheels


Type of vehicle

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%P

erce

nta

ge

B - AC - A

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 92



Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 93



Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 94


Pushcart 2 wheels


Type of vehicle

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 95



Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 96



Type of vehicle

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 97

A – 9. Intersection : Dr. Wahidin – Husein Hamzah


House

House

House

1.01.0

7.50

1.01.0

7.25

1.0

1.0

7.25

House

House

Market

House

HUSEIN HAMZAH STREET

DR. WAHIDIN STREET

N


A - 98




Road Environment Residential landuse with direct road side access for pedestrians andvehicles

Side Friction Residential low (100 – 299 activities per hour)Population 0.3 – 1.0 million inhabitantsTime of Measurement 06.35 – 07.35, 07.40 – 08.40Number ofUnmotorized 182 (7.3%)


792339

259301

438324

AC

B

12

3 4

56

[veh/h]



A - 99


5957555351494745434139373533312927252321191715131197531

1-minute interval

80

70

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]



A - 100

5855524946434037343128252219161310741

1-minute interval

100,0

90,0

80,0

70,0

60,0

50,0

40,0

30,0

20,0

10,0

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

54,0

41,4

33,1

28,0

24,2

21,6

18,8

17,0

15,3

13,7

12,3

11,1

10,18,9

7,9

6,9

5,8

4,7

3,4

1,6

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow




A - 101



Direction of flow

1.000

800

600

400

200

0

Flow

(veh

/h)

324

438

301

259

339

792

Flow of each stream


Direction of flow

250,00

200,00

150,00

100,00

50,00

0,00

Flo

w (

pcu

/h)

52,80

90,30

63,30

44,1051,60

164,60

Flow of each stream

23,8%

26,1%

50,1%




A - 102


Pushcart 2 wheels


Type of vehicle

2.500

2.000

1.500

1.000

500

0

Flow

(veh

/h)

1112

168

2.107

142

220


Pushcart 2 wheels


Type of vehicle

300,00

250,00

200,00

150,00

100,00

50,00

0,00

Flo

w (

pcu

/h)

0,700,507,20

33,60

210,70

142,00

6,00

66,00


Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 103




Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 104


Pushcart 2 wheels


Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 105



Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 106



Type of vehicle

800

700

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 107



Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 108


Pushcart 2 wheels


Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow



A - 109

A – 10. Intersection : W.R. Supratman – R. Suprapto


House

University

Public Service

Public Service

1.01.0

9.0

1.0

1.0

9.40

1.01.0

7.30

Hotel

Market

House

R. SUPRAPTO

STREET

W. R. SUPRATM

AN STREET

W. R. SUPRATMAN STREET

N


A - 110







159530

563439

279188

AC

B12

3 4

56

[veh/h]



A - 111


5957555351494745434139373533312927252321191715131197531

1-minute interval

60

50

40

30

20

10

0

Flow

(veh

/1-m

inut

e)

5957555351494745434139373533312927252321191715131197531

1-minute interval

15,0

14,0

13,0

12,0

11,0

10,0

9,0

8,0

7,0

6,0

5,0

4,0

3,0

2,0

1,0

0,0

Inte

rsec

tion

occu

panc

y (%

)Flow [Veh/1-min]



A - 112

5957555351494745434139373533312927252321191715131197531

1-minute interval

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow

35,0

26,9

24,2

22,5

21,0

19,7

18,6

17,6

16,7

15,9

15,1

14,4

13,6

12,9

12,3

11,7

11,2

10,6

10,09,4

8,6

7,7

6,3

2,1

Speed (km/h)

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Cum

ulat

ive

Perc

enta

ge


Direction of flow




A - 113



Direction of flow

800

700

600

500

400

300

200

100

0

Flow

(veh

/h)

188

279

439

563

530

159

Flow of each stream


Direction of flow

300,0

250,0

200,0

150,0

100,0

50,0

0,0F

low

(p

cu/h

)

73,4

117,9

188,7

233,1241,0

58,4

Flow of each stream

34,8%

44,9%

20,3%




A - 114


Pushcart 2 wheels


Type of vehicle

1.800

1.500

1.200

900

600

300

0

Flow

(veh

/h)

24364

1.525

547

13


Pushcart 2 wheels


Type of vehicle

700,0

600,0

500,0

400,0

300,0

200,0

100,0

0,0

Flo

w (

pcu

/h)

3,42,43,019,2

305,0

547,0

32,5


Pushcart 2 wheels


Type of vehicle

40,0

37,5

35,0

32,5

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 115




Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)

B - AC - A

Direction of flow


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

B - AC - A

Direction of flow


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

B - AC - A

Direction of flow



A - 116


Pushcart 2 wheels


Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 117


Pushcart 2 wheels


Type of vehicle

600

500

400

300

200

100

0

Flo

w (

veh

/h)

A - CB - C

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - CB - C

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - CB - C

Direction of flow



A - 118


Pushcart 2 wheels


Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 119


Pushcart 2 wheels


Type of vehicle

600

500

400

300

200

100

0

Flo

w (

veh

/h)


Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e


Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)


Direction of flow



A - 120


Pushcart 2 wheels


Type of vehicle

500

400

300

200

100

0

Flo

w (

veh

/h)

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

100,0%

90,0%

80,0%

70,0%

60,0%

50,0%

40,0%

30,0%

20,0%

10,0%

0,0%

Per

cen

tag

e

A - BC - B

Direction of flow

Pushcart 2 wheels


Type of vehicle

30,0

27,5

25,0

22,5

20,0

17,5

15,0

12,5

10,0

7,5

5,0

2,5

0,0

Mea

n Sp

eed

(km

/h)

A - BC - B

Direction of flow


APPENDIX B : Traffic Flow Composition

B - 1

Appendix B : Traffic Flow Composition

B. Traffic Flow Composition

B. 1 Traffic Composition of Each Stream :

Intersection 1 :


Percentage Vehicle CompositionTruck 3 axle 1.0 N.A N.A N.A 0.6 N.ATruck 2 axle 4.2 7.5 6.5 2.8 5.3 2.8Minibuses 0.2 N.A N.A N.A 0.4 0.4Car 16.5 15.9 11.5 24.5 16.2 21.8Motorcycle 76.9 75.4 80.2 69.2 75.9 73.0Bicycle 0.7 0.9 1.5 2.5 1.2 1.5Becak (Rickshaw) 0.6 0.3 0.4 1.1 0.4 0.4Tricycles N.A N.A N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A N.A 0.1

Percentage Movement26.1 6.9 10.4 12.3 28.1 16.2

Intersection 2 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A 0.2 N.A N.ATruck 2 axle 2.2 0.3 0.8 2.0 1.9 1.4Minibuses N.A 0.1 N.A N.A 0.1 N.ACar 8.9 10.9 3.4 23.1 7.4 22.3Motorcycle 85.0 86.0 92.1 72.7 85.8 73.5Bicycle 2.8 2.3 2.6 1.8 3.9 1.8Becak (Rickshaw) 0.7 0.3 1.0 0.2 1.0 0.9Tricycles 0.2 N.A 0.1 N.A N.A 0.2Pushcart (2-wheels) 0.1 N.A N.A N.A N.A N.A



B - 2

Intersection 3 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 1.6 N.A N.A 1.7 0.6 N.AMinibuses 0.5 N.A N.A N.A 0.7 N.ACar 7.6 1.9 3.8 5.2 11.8 N.AMotorcycle 85.4 94.2 91.5 87.0 83.8 3.4Bicycle 4.5 4.0 4.3 5.2 2.7 89.0Becak (Rickshaw) 0.1 N.A 0.2 0.9 0.4 6.8Tricycles 0.0 N.A 0.2 N.A N.A 0.8Pushcart (2-wheels) 0.1 N.A N.A N.A N.A N.A


Intersection 4 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 0.6 1.4 0.4 0.7 1.1 0.6Minibuses N.A N.A N.A 0.1 N.A N.ACar 6.3 2.4 1.6 9.0 5.5 8.0Motorcycle 90.0 92.4 91.9 88.1 86.8 86.4Bicycle 2.9 2.9 4.8 1.9 6.6 5.0Becak (Rickshaw) N.A N.A N.A N.A N.A 0.1Tricycles 0.1 0.5 0.4 N.A N.A N.APushcart (2-wheels) N.A 0.5 0.8 0.1 N.A N.A


Intersection 5 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 0.6 0.3 0.4 N.A 0.9 N.AMinibuses 0.2 N.A 0.4 N.A 0.4 N.ACar 12.7 10.9 12.5 7.3 13.1 4.6Motorcycle 81.8 86.6 84.1 87.2 84.3 91.3Bicycle 4.3 1.8 2.2 5.0 1.1 3.8Becak (Rickshaw) 0.3 0.4 0.3 N.A 0.2 0.4Tricycles 0.0 N.A 0.1 0.5 N.A N.APushcart (2-wheels) 0.0 N.A N.A N.A N.A N.A



B - 3

Intersection 6 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A 0.1 N.ATruck 2 axle 0.8 2.6 0.5 1.0 0.3 0.9Minibuses N.A N.A 0.5 0.2 0.1 0.1Car 16.0 13.2 5.9 14.0 12.6 22.2Motorcycle 79.7 81.5 91.1 82.9 82.4 76.0Bicycle 2.4 2.6 1.5 1.1 3.6 0.5Becak (Rickshaw) 1.0 N.A 0.5 0.6 0.8 0.3Tricycles N.A N.A N.A 0.1 0.1 N.APushcart (2-wheels) N.A N.A N.A 0.1 0.1 N.A


Intersection 7 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 3.1 0.1 N.A 2.3 0.5 0.3Minibuses 0.8 N.A 0.1 N.A 0.2 N.ACar 14.6 4.8 4.8 19.9 14.6 6.0Motorcycle 73.6 87.7 87.5 75.1 75.4 89.7Bicycle 6.1 6.0 4.2 2.7 7.9 3.9Becak (Rickshaw) 1.8 1.3 3.4 N.A 1.2 N.ATricycles N.A N.A N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A 0.1 N.A


Intersection 8 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A 0.3 2.2 0.2 1.8Minibuses N.A N.A N.A 1.7 N.A 2.4Car 4.2 7.0 6.5 13.4 3.6 13.9Motorcycle 89.9 89.4 87.8 78.2 87.6 80.1Bicycle 4.6 3.2 5.2 4.5 7.1 1.8Becak (Rickshaw) 0.9 0.2 0.3 N.A 1.4 N.ATricycles N.A 0.2 N.A N.A N.A N.APushcart (2-wheels) 0.4 N.A N.A N.A N.A N.A



B - 4

Intersection 9 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 1.5 0.6 0.8 0.7 0.5 N.AMinibuses N.A N.A N.A 0.7 N.A N.ACar 5.2 2.9 4.2 6.6 9.4 5.9Motorcycle 83.1 90.9 89.2 87.4 83.6 86.7Bicycle 9.6 5.3 5.4 3.3 6.4 6.8Becak (Rickshaw) 0.6 0.3 0.4 1.3 N.A 0.3Tricycles N.A N.A N.A N.A 0.2 N.APushcart (2-wheels) N.A N.A N.A N.A N.A 0.3


Intersection 10 :


Percentage Vehicle CompositionTruck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle N.A 0.4 N.A 1.6 1.1 0.5Minibuses N.A 30.2 N.A 23.7 23.3 20.2Car 19.5 66.8 26.5 71.8 72.8 70.2Motorcycle 73.6 2.3 71.8 2.7 1.8 8.5Bicycle 6.3 0.2 1.6 N.A 0.4 N.ABecak (Rickshaw) 0.6 0.2 N.A 0.2 0.4 N.ATricycles N.A N.A 0.2 N.A 0.4 0.5Pushcart (2-wheels) N.A N.A N.A N.A N.A N.A



B - 5

B. 2 Traffic Composition of Each Group of Conflict :

Intersection 1 :Group of conflict 1 :

StreamsC – A B – AType of Vehicle Flow

[veh/h] % Flow [veh/h] %

Truck 3 axle 12 0.7 N.A N.ATruck 2 axle 51 2.9 16 0.9Minibuses 2 0.1 N.A N.ACar 199 11.2 140 7.9Motorcycle 928 52.2 395 22.2Bicycle 8 0.5 14 0.8Becak (Rickshaw) 7 0.4 6 0.3Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

1206 571

Group of conflict 2 :Streams

C – B B – A A – C A – BType ofVehicle Flow


[veh/h] % Flow[veh/h] %

Truck 3 axle N.A N.A N.A N.A 8 0.3 N.A N.ATruck 2 axle 24 0.8 16 0.5 69 2.3 21 0.7Minibuses N.A N.A N.A N.A 5 0.2 3 0.1Car 51 1.7 140 4.8 211 7.2 163 5.5Motorcycle 242 8.2 395 13.4 987 33.6 546 18.6Bicycle 3 0.1 14 0.5 15 0.5 11 0.4Becak(Rickshaw) 1 0.0 6 0.2 5 0.2 3 0.1Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A 1 0.0

480 571 1300 748


B - 6


B – C A – CType of Vehicle Flow [veh/h] % Flow

[veh/h] %Truck 3 axle N.A N.A 8 0.4Truck 2 axle 31 1.7 69 3.9Minibuses N.A N.A 5 0.3Car 55 3.1 211 11.9Motorcycle 385 21.6 987 55.4Bicycle 7 0.4 15 0.8Becak (Rickshaw) 2 0.1 5 0.3Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

480 1300


C – A C – B B – A A – CType ofVehicle Flow



Truck 3 axle 12 0.4 N.A N.A N.A N.A 8 0.2Truck 2 axle 51 1.5 24 0.7 16 0.5 69 2.0Minibuses 2 0.1 N.A N.A N.A N.A 5 0.1Car 199 5.9 51 1.5 140 4.1 211 6.2Motorcycle 927 27.3 242 7.1 395 11.6 987 29.0Bicycle 8 0.2 3 0.1 14 0.4 15 0.4Becak(Rickshaw) 7 0.2 1 0.0 6 0.2 5 0.1Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels)

N.A N.A N.A N.A N.A N.A N.A N.A

1206 321 571 1300Group of conflict 5 :




[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A 8 0.3Truck 2 axle 24 0.9 31 1.2 16 0.6 69 2.6Minibuses N.A N.A N.A N.A N.A N.A 5 0.2Car 51 1.9 55 2.1 140 5.2 211 7.9Motorcycle 242 9.1 385 14.4 395 14.8 987 36.9Bicycle 3 0.1 7 0.3 14 0.5 15 0.6Becak(Rickshaw) 1 0.0 2 0.1 6 0.2 5 0.2Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

1206 321 571 1300


B - 7


C – B A – BType of Vehicle Flow [veh/h] % Flow

[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 24 2.2 21 2.0Minibuses N.A N.A 3 0.3Car 51 4.8 163 15.2Motorcycle 242 22.6 546 51.1Bicycle 3 0.3 11 1.0Becak (Rickshaw) 1 0.1 3 0.3Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A 1 0.1

321 748




Truck 3 axle N.A N.A 1 0.1Truck 2 axle 24 1.5 11 0.7Minibuses N.A N.A N.A N.ACar 98 6.0 127 7.7Motorcycle 933 56.7 399 24.2Bicycle 31 1.9 10 0.6Becak (Rickshaw) 8 0.5 1 0.1Tricycles 2 0.1 N.A N.APushcart (2-wheels) 1 0.1 N.A N.A

1097 549





Truck 3 axle N.A N.A 1 0.0 N.A N.A N.A N.ATruck 2 axle 2 0.1 11 0.4 24 0.8 8 0.3Minibuses 1 0.0 N.A N.A 1 0.0 N.A N.ACar 74 2.4 127 4.2 91 3.0 127 4.2Motorcycle 586 19.3 399 13.1 1062 35.0 418 13.8Bicycle 16 0.5 10 0.3 48 1.6 10 0.3Becak(Rickshaw) 2 0.1 1 0.0 12 0.4 5 0.2Tricycles N.A N.A N.A N.A N.A N.A 1 0.0Pushcart(2-wheels)

N.A N.A N.A N.A N.A N.A N.A N.A

681 549 1238 569


B - 8



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 6 0.3 24 1.2Minibuses N.A N.A 1 0.0Car 27 1.3 91 4.5Motorcycle 732 36.0 1062 52.2Bicycle 21 1.0 48 2.4Becak (Rickshaw) 8 0.4 12 0.6Tricycles 1 0.0 N.A N.APushcart (2-wheels) N.A N.A N.A N.A

795 1238





Truck 3 axle N.A N.A N.A N.A 1 0.0 N.A N.ATruck 2 axle 24 0.7 2 0.1 11 0.3 24 0.7Minibuses N.A N.A 1 0.0 N.A N.A 1 0.0Car 98 2.7 74 2.1 127 3.6 91 2.6Motorcycle 933 26.2 586 16.4 399 11.2 1062 29.8Bicycle 31 0.9 16 0.4 10 0.3 48 1.3Becak(Rickshaw) 8 0.2 2 0.1 1 0.0 12 0.3Tricycles 2 0.1 N.A N.A N.A N.A N.A N.APushcart(2-wheels) 1 0.0 N.A N.A N.A N.A N.A N.A





[veh/h] %

Truck 3 axle N.A N.A N.A N.A 1 0.0 N.A N.ATruck 2 axle 2 0.1 6 0.2 11 0.3 24 0.7Minibuses 1 0.0 N.A N.A N.A N.A 1 0.0Car 74 2.3 27 0.8 127 3.9 91 2.8Motorcycle 586 18.0 732 22.4 399 12.2 1062 32.5Bicycle 16 0.5 21 0.6 10 0.3 48 1.5Becak(Rickshaw) 2 0.1 8 0.2 1 0.0 12 0.4Tricycles N.A N.A 1 0.0 N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

681 795 549 1238


B - 9



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 2 0.2 8 0.6Minibuses 1 0.1 N.A N.ACar 74 5.9 127 10.2Motorcycle 586 46.9 418 33.4Bicycle 16 1.3 10 0.8Becak (Rickshaw) 2 0.2 5 0.4Tricycles N.A N.A 1 0.1Pushcart (2-wheels) N.A N.A N.A N.A

681 569




Truck 3 axle N.A N.A N.A N.ATruck 2 axle 39 1.6 2 0.1Minibuses 13 0.5 N.A N.ACar 182 7.3 6 0.2Motorcycle 2033 81.5 100 4.0Bicycle 107 4.3 6 0.2Becak (Rickshaw) 3 0.1 1 0.0Tricycles 1 0.0 N.A N.APushcart (2-wheels) 3 0.1 N.A N.A

2381 115





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A 2 0.1 7 0.3 N.A N.AMinibuses N.A N.A N.A N.A 8 0.4 N.A N.ACar 13 0.6 6 0.3 135 6.5 4 0.2Motorcycle 660 31.8 100 4.8 956 46.1 105 5.1Bicycle 28 1.3 6 0.3 31 1.5 8 0.4Becak(Rickshaw) N.A N.A 1 0.0 4 0.2 1 0.0Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

701 115 1141 118


B - 10



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle N.A N.A 7 0.4Minibuses N.A N.A 8 0.5Car 17 1.1 135 8.5Motorcycle 408 25.7 956 60.2Bicycle 19 1.2 31 2.0Becak (Rickshaw) 1 0.1 4 0.3Tricycles 1 0.1 N.A N.APushcart (2-wheels) N.A N.A N.A N.A

446 1141





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 39 0.9 N.A N.A 2 0.0 7 0.2Minibuses 13 0.3 N.A N.A N.A N.A 8 0.2Car 182 4.2 13 0.3 6 0.1 135 3.1Motorcycle 2033 46.9 660 15.2 100 2.3 956 22.0Bicycle 107 2.5 28 0.6 6 0.1 31 0.7Becak(Rickshaw) 3 0.1 N.A N.A 1 0.0 4 0.1Tricycles 1 0.0 N.A N.A N.A N.A N.A N.APushcart(2-wheels) 3 0.1 N.A N.A N.A N.A N.A N.A





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A N.A N.A 2 0.1 7 0.3Minibuses N.A N.A N.A N.A N.A N.A 8 0.3Car 13 0.5 17 0.7 6 0.2 135 5.6Motorcycle 660 27.5 408 17.0 100 4.2 956 39.8Bicycle 28 1.2 19 0.8 6 0.2 31 1.3Becak(Rickshaw) N.A N.A 1 0.0 1 0.0 4 0.2Tricycles N.A N.A 1 0.0 N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

701 446 115 1141


B - 11



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle N.A N.A N.A N.AMinibuses N.A N.A N.A N.ACar 13 1.6 4 0.5Motorcycle 660 80.6 105 12.8Bicycle 28 3.4 8 1.0Becak (Rickshaw) N.A N.A 1 0.1Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

701 118




Truck 3 axle N.A N.A N.A N.ATruck 2 axle 4 0.3 5 0.4Minibuses N.A N.A 1 0.1Car 45 3.2 61 4.4Motorcycle 641 46.0 600 43.1Bicycle 21 1.5 13 0.9Becak (Rickshaw) N.A N.A N.A N.ATricycles 1 0.1 N.A N.APushcart (2-wheels) N.A N.A 1 0.1

712 681





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 3 0.1 5 0.2 11 0.4 5 0.2Minibuses N.A N.A 1 0.0 N.A N.A N.A N.ACar 5 0.2 61 2.2 54 2.0 71 2.6Motorcycle 194 7.0 600 21.7 856 31.0 766 27.7Bicycle 6 0.2 13 0.5 65 2.4 44 1.6Becak(Rickshaw) N.A N.A N.A N.A N.A N.A 1 0.0Tricycles 1 0.0 N.A N.A N.A N.A N.A N.APushcart(2-wheels) 1 0.0 1 0.0 N.A N.A N.A N.A

210 681 986 887


B - 12



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 1 0.1 11 0.9Minibuses N.A N.A N.A N.ACar 4 0.3 54 4.4Motorcycle 228 18.5 856 69.4Bicycle 12 1.0 65 5.3Becak (Rickshaw) N.A N.A N.A N.ATricycles 1 0.1 N.A N.APushcart (2-wheels) 2 0.2 N.A N.A

248 986





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 4 0.2 3 0.1 5 0.2 11 0.4Minibuses N.A N.A N.A N.A 1 0.0 N.A N.ACar 45 1.7 5 0.2 61 2.4 54 2.1Motorcycle 641 24.8 194 7.5 600 23.2 856 33.1Bicycle 21 0.8 6 0.2 13 0.5 65 2.5Becak(Rickshaw) N.A N.A N.A N.A N.A N.A N.A N.ATricycles 1 0.0 1 0.0 N.A N.A N.A N.APushcart(2-wheels) N.A N.A 1 0.0 1 0.0 N.A N.A





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 3 0.1 1 0.0 5 0.2 11 0.5Minibuses N.A N.A N.A N.A 1 0.0 N.A N.ACar 5 0.2 4 0.2 61 2.9 54 2.5Motorcycle 194 9.1 228 10.7 600 28.2 856 40.3Bicycle 6 0.3 12 0.6 13 0.6 65 3.1Becak(Rickshaw) N.A N.A N.A N.A N.A N.A N.A N.ATricycles 1 0.0 1 0.0 N.A N.A N.A N.APushcart(2-wheels) 1 0.0 2 0.1 1 0.0 N.A N.A

210 248 681 986


B - 13



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 3 0.3 5 0.5Minibuses N.A N.A N.A N.ACar 5 0.5 71 6.5Motorcycle 194 17.7 766 69.8Bicycle 6 0.5 44 4.0Becak (Rickshaw) N.A N.A 1 0.1Tricycles 1 0.1 N.A N.APushcart (2-wheels) 1 0.1 N.A N.A

210 887




Truck 3 axle N.A N.A N.A N.ATruck 2 axle 16 0.6 N.A N.AMinibuses 5 0.2 N.A N.ACar 324 11.7 16 0.6Motorcycle 2081 75.3 191 6.9Bicycle 110 4.0 11 0.4Becak (Rickshaw) 7 0.3 N.A N.ATricycles 1 0.0 1 0.0Pushcart (2-wheels) 1 0.0 N.A N.A

2545 219





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 3 0.1 N.A N.A 21 0.6 N.A N.AMinibuses N.A N.A N.A N.A 8 0.2 N.A N.ACar 112 3.0 16 0.4 290 7.8 11 0.3Motorcycle 892 24.0 191 5.1 1872 50.5 219 5.9Bicycle 19 0.5 11 0.3 25 0.7 9 0.2Becak(Rickshaw) 4 0.1 N.A N.A 4 0.1 1 0.0Tricycles N.A N.A 1 0.0 N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

1030 219 2220 240


B - 14



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 4 0.1 21 0.7Minibuses 4 0.1 8 0.2Car 123 3.8 290 9.0Motorcycle 829 25.9 1872 58.4Bicycle 22 0.7 25 0.8Becak (Rickshaw) 3 0.1 4 0.1Tricycles 1 0.0 N.A N.APushcart (2-wheels) N.A N.A N.A N.A

986 2220





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 16 0.3 3 0.0 N.A N.A 21 0.3Minibuses 5 0.1 N.A N.A N.A N.A 8 0.1Car 324 5.4 112 1.9 16 0.3 290 4.8Motorcycle 2081 34.6 892 14.8 191 3.2 1872 31.1Bicycle 110 1.8 19 0.3 11 0.2 25 0.4Becak(Rickshaw) 7 0.1 4 0.1 N.A N.A 4 0.1Tricycles 1 0.0 N.A N.A 1 0.0 N.A N.APushcart(2-wheels) 1 0.0 N.A N.A N.A N.A N.A N.A





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 3 0.1 4 0.1 N.A N.A 21 0.5Minibuses N.A N.A 4 0.1 N.A N.A 8 0.2Car 112 2.5 123 2.8 16 0.4 290 6.5Motorcycle 892 20.0 829 18.6 191 4.3 1872 42.0Bicycle 19 0.4 22 0.5 11 0.2 25 0.6Becak(Rickshaw) 4 0.1 3 0.1 N.A N.A 4 0.1Tricycles N.A N.A 1 0.0 1 0.0 N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

1030 986 219 2220


B - 15



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 3 0.2 N.A N.AMinibuses N.A N.A N.A N.ACar 112 8.8 11 0.9Motorcycle 892 70.2 219 17.2Bicycle 19 1.5 9 0.7Becak (Rickshaw) 4 0.3 1 0.1Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

1030 240




Truck 3 axle N.A N.A N.A N.ATruck 2 axle 12 0.5 9 0.4Minibuses N.A N.A 2 0.1Car 229 9.9 124 5.4Motorcycle 1139 49.2 735 31.7Bicycle 34 1.5 10 0.4Becak (Rickshaw) 15 0.6 5 0.2Tricycles N.A N.A 1 0.0Pushcart (2-wheels) N.A N.A 1 0.0

1429 887





Truck 3 axle N.A N.A N.A N.A 1 0.0 N.A N.ATruck 2 axle 4 0.1 9 0.3 5 0.1 8 0.2Minibuses N.A N.A 2 0.1 2 0.1 1 0.0Car 20 0.6 124 3.5 198 5.6 206 5.8Motorcycle 123 3.5 735 20.8 1298 36.6 706 19.9Bicycle 4 0.1 10 0.3 57 1.6 5 0.1Becak(Rickshaw) N.A N.A 5 0.1 12 0.3 3 0.1Tricycles N.A N.A 1 0.0 1 0.0 N.A N.APushcart(2-wheels) N.A N.A 1 0.0 1 0.0 N.A N.A

151 887 1575 929


B - 16



[veh/h] %Truck 3 axle N.A N.A 1 0.1Truck 2 axle 1 0.1 5 0.3Minibuses 1 0.1 2 0.1Car 12 0.7 198 11.1Motorcycle 185 10.4 1298 73.0Bicycle 3 0.2 57 3.2Becak (Rickshaw) 1 0.1 12 0.7Tricycles N.A N.A 1 0.1Pushcart (2-wheels) N.A N.A 1 0.1

203 1575





Truck 3 axle N.A N.A N.A N.A N.A N.A 1 0.0Truck 2 axle 12 0.3 4 0.1 9 0.2 5 0.1Minibuses N.A N.A N.A N.A 2 0.0 2 0.0Car 229 5.7 20 0.5 124 3.1 198 4.9Motorcycle 1139 28.2 123 3.0 735 18.2 1298 32.1Bicycle 34 0.8 4 0.1 10 0.2 57 1.4Becak(Rickshaw) 15 0.4 N.A N.A 5 0.1 12 0.3Tricycles N.A N.A N.A N.A 1 0.0 1 0.0Pushcart(2-wheels) N.A N.A N.A N.A 1 0.0 1 0.0





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A 1 0.0Truck 2 axle 4 0.1 1 0.0 9 0.3 5 0.2Minibuses N.A N.A 1 0.0 2 0.1 2 0.1Car 20 0.7 12 0.4 124 4.4 198 7.0Motorcycle 123 4.4 185 6.6 735 26.1 1298 46.1Bicycle 4 0.1 3 0.1 10 0.4 57 2.0Becak(Rickshaw) N.A N.A 1 0.0 5 0.2 12 0.4Tricycles N.A N.A N.A N.A 1 0.0 1 0.0Pushcart(2-wheels) N.A N.A N.A N.A 1 0.0 1 0.0

151 203 887 1575


B - 17



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 4 0.4 8 0.7Minibuses N.A N.A 1 0.1Car 20 1.9 206 19.1Motorcycle 123 11.4 706 65.4Bicycle 4 0.4 5 0.5Becak (Rickshaw) N.A N.A 3 0.3Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

151 929




Truck 3 axle N.A N.A N.A N.ATruck 2 axle 26 2.4 6 0.5Minibuses 7 0.6 N.A N.ACar 123 11.2 52 4.7Motorcycle 619 56.2 196 17.8Bicycle 51 4.6 7 0.6Becak (Rickshaw) 15 1.4 N.A N.ATricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

841 261





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 1 0.0 6 0.3 4 0.2 1 0.0Minibuses N.A N.A N.A N.A 2 0.1 N.A N.ACar 40 1.8 52 2.3 117 5.3 20 0.9Motorcycle 726 32.7 196 8.8 604 27.2 297 13.4Bicycle 50 2.3 7 0.3 63 2.8 13 0.6Becak(Rickshaw) 11 0.5 N.A N.A 10 0.5 N.A N.ATricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A 1 0.0 N.A N.A

828 261 801 331


B - 18



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle N.A N.A 4 0.3Minibuses 1 0.1 2 0.1Car 32 2.2 117 7.9Motorcycle 588 39.9 604 41.0Bicycle 28 1.9 63 4.3Becak (Rickshaw) 23 1.6 10 0.7Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A 1 0.1

672 801





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 26 1.0 1 0.0 6 0.2 4 0.1Minibuses 7 0.3 N.A N.A N.A N.A 2 0.1Car 123 4.5 40 1.5 52 1.9 117 4.3Motorcycle 619 22.7 726 26.6 196 7.2 604 22.1Bicycle 51 1.9 50 1.8 7 0.3 63 2.3Becak(Rickshaw) 15 0.5 11 0.4 N.A N.A 10 0.4Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A 1 0.0





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 1 0.0 N.A N.A 6 0.2 4 0.2Minibuses N.A N.A 1 0.0 N.A N.A 2 0.1Car 40 1.6 32 1.2 52 2.0 117 4.6Motorcycle 726 28.3 588 23.0 196 7.7 604 23.6Bicycle 50 2.0 28 1.1 7 0.3 63 2.5Becak(Rickshaw) 11 0.4 23 0.9 N.A N.A 10 0.4Tricycles N.A N.A N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A 1 0.0

828 672 261 801


B - 19



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 1 0.1 1 0.1Minibuses N.A N.A N.A N.ACar 40 3.5 20 1.7Motorcycle 726 62.6 297 25.6Bicycle 50 4.3 13 1.1Becak (Rickshaw) 11 0.9 N.A N.ATricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

828 331




Truck 3 axle N.A N.A N.A N.ATruck 2 axle N.A N.A 4 0.6Minibuses N.A N.A 3 0.5Car 19 3.0 24 3.8Motorcycle 409 64.5 140 22.1Bicycle 21 3.3 8 1.3Becak (Rickshaw) 4 0.6 N.A N.ATricycles N.A N.A N.A N.APushcart (2-wheels) 2 0.3 N.A N.A

455 179





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A 4 0.3 1 0.1 3 0.2Minibuses N.A N.A 3 0.2 N.A N.A 4 0.3Car 31 2.6 24 2.0 15 1.2 23 1.9Motorcycle 396 32.8 140 11.6 369 30.5 133 11.0Bicycle 14 1.2 8 0.7 30 2.5 3 0.2Becak(Rickshaw) 1 0.1 N.A N.A 6 0.5 N.A N.ATricycles 1 0.1 N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

443 179 421 166


B - 20



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 2 0.2 1 0.1Minibuses N.A N.A N.A N.ACar 45 4.0 15 1.3Motorcycle 609 54.6 369 33.1Bicycle 36 3.2 30 2.7Becak (Rickshaw) 2 0.2 6 0.5Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

694 421





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A N.A N.A 4 0.3 1 0.1Minibuses N.A N.A N.A N.A 3 0.2 N.A N.ACar 19 1.3 31 2.1 24 1.6 15 1.0Motorcycle 409 27.3 396 26.4 140 9.3 369 24.6Bicycle 21 1.4 14 0.9 8 0.5 30 2.0Becak(Rickshaw) 4 0.3 1 0.1 N.A N.A 6 0.4Tricycles N.A N.A 1 0.1 N.A N.A N.A N.APushcart(2-wheels) 2 0.1 N.A N.A N.A N.A N.A N.A





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A 2 0.1 4 0.2 1 0.1Minibuses N.A N.A N.A N.A 3 0.2 N.A N.ACar 31 1.8 45 2.6 24 1.4 15 0.9Motorcycle 396 22.8 609 35.1 140 8.1 369 21.2Bicycle 14 0.8 36 2.1 8 0.5 30 1.7Becak(Rickshaw) 1 0.1 2 0.1 N.A N.A 6 0.3Tricycles 1 0.1 N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

443 694 179 421


B - 21



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle N.A N.A 3 0.5Minibuses N.A N.A 4 0.7Car 31 5.1 23 3.8Motorcycle 396 65.0 133 21.8Bicycle 14 2.3 3 0.5Becak (Rickshaw) 1 0.2 N.A N.ATricycles 1 0.2 N.A N.APushcart (2-wheels) N.A N.A N.A N.A

443 166




Truck 3 axle N.A N.A N.A N.ATruck 2 axle 12 1.1 2 0.2Minibuses N.A N.A 2 0.2Car 41 3.8 20 1.8Motorcycle 658 60.2 263 24.1Bicycle 76 7.0 10 0.9Becak (Rickshaw) 5 0.5 4 0.4Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A

792 301





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 2 0.1 2 0.1 2 0.1 N.A N.AMinibuses N.A N.A 2 0.1 N.A N.A N.A N.ACar 10 0.7 20 1.4 41 2.9 19 1.4Motorcycle 308 22.0 263 18.8 366 26.1 281 20.0Bicycle 18 1.3 10 0.7 28 2.0 22 1.6Becak(Rickshaw) 1 0.1 4 0.3 N.A N.A 1 0.1Tricycles N.A N.A N.A N.A 1 0.1 N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A N.A 1 0.1

339 301 438 324


B - 22



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 2 0.3 2 0.3Minibuses N.A N.A N.A N.ACar 11 1.6 41 5.9Motorcycle 231 33.1 366 52.5Bicycle 14 2.0 28 4.0Becak (Rickshaw) 1 0.1 N.A N.ATricycles N.A N.A 1 0.1Pushcart (2-wheels) N.A N.A N.A N.A

259 438





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 12 0.6 2 0.1 2 0.1 2 0.1Minibuses N.A N.A N.A N.A 2 0.1 N.A N.ACar 41 2.2 10 0.5 20 1.1 41 2.2Motorcycle 658 35.2 308 16.5 263 14.1 366 19.6Bicycle 76 4.1 18 1.0 10 0.5 28 1.5Becak(Rickshaw) 5 0.3 1 0.1 4 0.2 N.A N.ATricycles N.A N.A N.A N.A N.A N.A 1 0.1Pushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 2 0.1 2 0.1 2 0.1 2 0.1Minibuses N.A N.A N.A N.A 2 0.1 N.A N.ACar 10 0.7 11 0.8 20 1.5 41 3.1Motorcycle 308 23.0 231 17.3 263 19.7 366 27.4Bicycle 18 1.3 14 1.0 10 0.7 28 2.1Becak(Rickshaw) 1 0.1 1 0.1 4 0.3 N.A N.ATricycles N.A N.A N.A N.A N.A N.A 1 0.1Pushcart(2-wheels) N.A N.A N.A N.A N.A N.A N.A N.A

339 259 301 438


B - 23



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 2 0.3 N.A N.AMinibuses N.A N.A N.A N.ACar 10 1.5 19 2.9Motorcycle 308 46.5 281 42.4Bicycle 18 2.7 22 3.3Becak (Rickshaw) 1 0.2 1 0.2Tricycles N.A N.A N.A N.APushcart (2-wheels) N.A N.A 1 0.2

339 324




Truck 3 axle N.A N.A N.A N.ATruck 2 axle N.A N.A 7 1.2Minibuses N.A N.A N.A N.ACar 31 5.2 104 17.4Motorcycle 117 19.6 315 52.7Bicycle 10 1.7 12 2.0Becak (Rickshaw) 1 0.2 N.A N.ATricycles N.A N.A 1 0.2Pushcart (2-wheels) N.A N.A N.A N.A

159 439





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 2 0.1 7 0.5 3 0.2 1 0.1Minibuses N.A N.A N.A N.A N.A N.A N.A N.ACar 160 11.1 104 7.2 65 4.5 38 2.6Motorcycle 354 24.7 315 21.9 203 14.1 132 9.2Bicycle 12 0.8 12 0.8 5 0.3 16 1.1Becak(Rickshaw) 1 0.1 N.A N.A 1 0.1 N.A N.ATricycles 1 0.1 1 0.1 1 0.1 N.A N.APushcart(2-wheels) N.A N.A N.A N.A 1 0.1 1 0.1

530 439 279 188


B - 24



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle N.A N.A 3 0.4Minibuses N.A N.A N.A N.ACar 149 17.7 65 7.7Motorcycle 404 48.0 203 24.1Bicycle 9 1.1 5 0.6Becak (Rickshaw) N.A N.A 1 0.1Tricycles 1 0.1 1 0.1Pushcart (2-wheels) N.A N.A 1 0.1

563 279





Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A 2 0.1 7 0.5 3 0.2Minibuses N.A N.A N.A N.A N.A N.A N.A N.ACar 31 2.2 160 11.4 104 7.4 65 4.6Motorcycle 117 8.3 354 25.2 315 22.4 203 14.4Bicycle 10 0.7 12 0.9 12 0.9 5 0.4Becak(Rickshaw) 1 0.1 1 0.1 N.A N.A 1 0.1Tricycles N.A N.A 1 0.1 1 0.1 1 0.1Pushcart(2-wheels) N.A N.A N.A N.A N.A N.A 1 0.1





[veh/h] %

Truck 3 axle N.A N.A N.A N.A N.A N.A N.A N.ATruck 2 axle 2 0.1 N.A N.A 7 0.4 3 0.2Minibuses N.A N.A N.A N.A N.A N.A N.A N.ACar 160 8.8 149 8.2 104 5.7 65 3.6Motorcycle 354 19.5 404 22.3 315 17.4 203 11.2Bicycle 12 0.7 9 0.5 12 0.7 5 0.3Becak(Rickshaw) 1 0.1 N.A N.A N.A N.A 1 0.1Tricycles 1 0.1 1 0.1 1 0.1 1 0.1Pushcart(2-wheels) N.A N.A N.A N.A N.A N.A 1 0.1

530 563 439 279


B - 25



[veh/h] %Truck 3 axle N.A N.A N.A N.ATruck 2 axle 2 0.3 1 0.1Minibuses N.A N.A N.A N.ACar 160 22.3 38 5.3Motorcycle 354 49.3 132 18.4Bicycle 12 1.7 16 2.2Becak (Rickshaw) 1 0.1 N.A N.ATricycles 1 0.1 N.A N.APushcart (2-wheels) N.A N.A 1 0.1

530 188

APPENDIX C : Mean Speed and Passenger Car Units of Each Stream

C - 1

Appendix C : Mean Speed and Passenger Car Units of Each Stream

C. Mean Speed And Passenger Car Units of Each Traffic Stream :

C. 1 Mean Speed of Each Traffic Stream

Intersection 1 :


Speed [km/h]Truck 3 axle 27.0 N.A N.A N.A 21.4 N.ATruck 2 axle 26.5 11.2 10.9 16.6 22.1 18.7Minibuses 27.1 N.A N.A N.A 17.7 21.4Car 27.6 11.6 11.9 17.6 21.9 21.4Motorcycle 31.8 14.9 17.9 22.6 25.9 28.2Bicycle 16.4 9.3 12.1 12.8 17.3 17.6Becak (Rickshaw) 13.2 5.0 7.3 8.4 13.4 13.8Tricycles N.A N.A N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A N.A 5.4

Mean Speed [km/h]30.6 14.0 16.6 20.8 24.9 26.2

Intersection 2 :


Speed [km/h]Truck 3 axle N.A N.A N.A 6.8 N.A N.ATruck 2 axle 24.8 22.8 7.0 12.7 17.5 31.4Minibuses N.A 14.9 N.A N.A 12.3 N.ACar 23.9 16.8 9.3 13.5 18.1 30.3Motorcycle 26.4 20.9 13.3 19.1 20.8 37.7Bicycle 14.2 13.3 11.5 13.5 12.0 25.4Becak (Rickshaw) 11.3 9.3 7.0 6.5 10.1 15.9Tricycles 9.7 N.A 7.0 N.A N.A 20.6Pushcart (2-wheels) 6.1 N.A N.A N.A N.A N.A

Mean Speed [km/h]25.7 20.3 13.0 17.5 20.1 35.6


C - 2

Intersection 3 :


Speed [km/h]Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 18.3 N.A N.A 4.4 11.6 N.AMinibuses 18.5 N.A N.A N.A 14.6 N.ACar 16.9 9.2 5.3 7.1 13.3 16.3Motorcycle 21.3 15.1 9.4 22.1 15.0 16.8Bicycle 13.8 12.7 6.8 20.3 10.0 11.1Becak (Rickshaw) 10.6 N.A 4.2 12.3 7.5 9.0Tricycles 2.7 N.A 4.7 N.A N.A N.APushcart (2-wheels) 5.1 N.A N.A N.A N.A N.A

Mean Speed [km/h]20.5 14.9 9.1 20.9 14.6 16.4

Intersection 4 :


Speed [km/h]Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 18.1 17.5 9.2 8.1 20.2 9.9Minibuses N.A N.A N.A 7.7 N.A N.ACar 20.0 11.4 6.2 9.2 16.7 12.5Motorcycle 23.1 17.8 12.3 13.2 20.2 17.4Bicycle 13.6 14.5 10.2 7.1 14.3 15.4Becak (Rickshaw) N.A N.A N.A N.A N.A 6.5Tricycles 10.5 16.7 6.5 N.A N.A N.APushcart (2-wheels) N.A 2.0 3.7 6.6 N.A N.A

Mean Speed [km/h]22.6 17.5 12.0 12.7 19.6 16.9

Intersection 5 :


Speed [km/h]Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 24.1 9.0 4.6 N.A 18.6 N.AMinibuses 28.7 N.A 5.4 N.A 20.2 N.ACar 24.0 10.8 6.8 6.7 18.0 12.3Motorcycle 25.9 15.2 12.5 20.4 21.2 18.0Bicycle 14.0 11.8 9.7 23.4 13.9 12.7Becak (Rickshaw) 9.5 6.8 6.8 N.A 7.4 9.6Tricycles 7.0 N.A 5.3 9.3 N.A N.APushcart (2-wheels) 4.5 N.A N.A N.A N.A N.A

Mean Speed [km/h]25.0 14.6 11.7 19.5 20.7 17.5


C - 3

Intersection 6 :


Speed [km/h]Truck 3 axle N.A N.A N.A N.A 10.5 N.ATruck 2 axle 11.7 10.5 5.2 6.0 11.3 8.4Minibuses N.A N.A 6.2 8.0 7.8 6.6Car 10.7 10.3 6.2 7.8 9.9 8.8Motorcycle 13.4 16.2 10.2 11.4 11.4 10.5Bicycle 6.9 14.2 7.3 8.7 6.3 8.2Becak (Rickshaw) 5.0 N.A 4.6 8.3 4.7 5.1Tricycles N.A N.A N.A 8.2 5.5 N.APushcart (2-wheels) N.A N.A N.A 2.6 2.3 N.A

Mean Speed [km/h]12.7 15.2 9.8 10.8 10.9 10.0

Intersection 7:


Speed [km/h]Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 20.6 14.8 N.A 11.7 21.1 7.6Minibuses 25.4 N.A 10.5 N.A 21.8 N.ACar 19.8 15.0 7.4 11.9 16.8 11.0Motorcycle 25.3 17.8 10.3 15.0 20.9 14.4Bicycle 12.7 12.7 7.1 11.1 10.6 11.1Becak (Rickshaw) 10.4 11.6 6.8 N.A 7.4 N.ATricycles N.A N.A N.A N.A N.A N.APushcart (2-wheels) N.A N.A N.A N.A 3.7 N.A

Mean Speed [km/h]23.3 17.3 9.9 14.2 19.3 14.0

Intersection 8 :


Speed [km/h]Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A 5.6 12.1 15.8 11.1Minibuses N.A N.A N.A 13.1 N.A 6.6Car 16.9 11.8 6.2 15.0 13.1 11.7Motorcycle 19.5 14.9 9.6 25.0 16.9 26.6Bicycle 10.7 9.5 8.4 13.8 8.4 23.4Becak (Rickshaw) 7.8 11.7 6.6 N.A 7.0 N.ATricycles N.A 7.2 N.A N.A N.A N.APushcart (2-wheels) 3.8 N.A N.A N.A N.A N.A

Mean Speed [km/h]18.9 14.5 9.3 22.6 16.0 23.7


C - 4

Intersection 9 :


Speed [km/h]Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 25.8 27.4 11.3 12.1 25.7 N.AMinibuses N.A N.A N.A 8.7 N.A N.ACar 26.1 31.9 20.1 16.6 22.0 15.6Motorcycle 30.2 53.8 45.1 23.1 26.4 21.9Bicycle 13.2 59.1 53.3 24.8 12.0 15.3Becak (Rickshaw) 10.0 18.8 22.1 7.9 N.A 7.8Tricycles N.A N.A N.A N.A 11.5 N.APushcart (2-wheels) N.A N.A N.A N.A N.A 5.5

Mean Speed [km/h]28.2 53.2 44.2 22.4 25.0 21.0

Intersection 10 :


Speed [km/h]Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 16.7 N.A 14.9 19.6 11.6 N.AMinibuses N.A N.A N.A N.A N.A N.ACar 20.9 15.3 12.7 13.1 19.3 14.0Motorcycle 21.8 17.7 15.9 15.5 20.5 17.8Bicycle 9.4 9.5 11.0 12.7 6.8 11.5Becak (Rickshaw) 8.0 5.2 N.A N.A 6.9 N.ATricycles N.A 6.1 6.2 5.5 6.2 N.APushcart (2-wheels) N.A N.A N.A N.A 2.1 3.4

Mean Speed [km/h]20.8 16.7 15.0 14.8 19.8 16.4


C - 5

C. 2 Passenger Car Units of Each Traffic Stream

Intersection 1 :


Truck 3 axle 2.7 N.A N.A N.A 2.7 N.ATruck 2 axle 2.8 2.8 2.9 2.8 2.6 3.1Minibuses 1.6 N.A N.A N.A 2.0 1.6Car 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.1 0.2 0.2 0.1Bicycle 0.3 0.2 0.2 0.2 0.2 0.2Becak(Rickshaw) 0.7 0.8 0.6 0.7 0.6 0.5

Tricycles N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A N.A 1.0

Intersection 2 :


Truck 3 axle N.A N.A N.A 5.3 N.A N.ATruck 2 axle 2.6 2.0 3.5 2.8 2.7 2.6Minibuses N.A 1.8 N.A N.A 2.3 N.ACar 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.1 0.1 0.2 0.2Bicycle 0.3 0.2 0.1 0.2 0.2 0.2Becak(Rickshaw) 0.7 0.6 0.5 0.7 0.6 0.7

Tricycles 0.6 N.A 0.3 N.A N.A 0.4Pushcart(2-wheels) 1.0 N.A N.A N.A N.A N.A

Intersection 3 :


Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 2.5 N.A N.A 4.3 3.1 N.AMinibuses 1.4 N.A N.A N.A 1.4 N.ACar 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.1 0.1 0.1 0.2 0.2Bicycle 0.2 0.1 0.1 0.1 0.2 0.2Becak(Rickshaw) 0.6 N.A 0.4 0.2 0.6 0.6

Tricycles 1.6 N.A 0.3 N.A N.A N.APushcart(2-wheels) 0.9 N.A N.A N.A N.A N.A


C - 6

Intersection 4 :


Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 2.9 1.7 1.8 3.0 2.2 3.4Minibuses N.A N.A N.A 1.9 N.A N.ACar 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.1 0.1 0.1 0.2 0.1Bicycle 0.2 0.1 0.1 0.2 0.2 0.1Becak(Rickshaw) N.A N.A N.A N.A N.A 0.7

Tricycles 0.5 0.2 0.2 N.A N.A N.APushcart(2-wheels) N.A 1.5 0.4 0.4 N.A N.A

Intersection 5 :


Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 2.7 3.2 4.0 N.A 2.6 N.AMinibuses 1.3 N.A 2.0 N.A 1.4 N.ACar 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.1 0.1 0.1 0.2 0.1Bicycle 0.3 0.1 0.1 0.0 0.2 0.2Becak(Rickshaw) 0.9 0.6 0.3 N.A 0.8 0.4

Tricycles 0.9 N.A 0.3 0.2 N.A N.APushcart(2-wheels) 1.4 N.A N.A N.A N.A N.A

Intersection 6 :


Truck 3 axle N.A N.A N.A N.A 2.5 N.ATruck 2 axle 2.4 2.6 3.2 3.5 2.3 2.8Minibuses N.A N.A 1.6 1.6 2.0 2.1Car 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.1 0.1 0.1 0.2 0.2Bicycle 0.3 0.1 0.1 0.1 0.3 0.2Becak(Rickshaw) 0.7 N.A 0.5 0.3 0.7 0.6

Tricycles N.A N.A N.A 0.2 0.5 N.APushcart(2-wheels) N.A N.A N.A 0.8 1.1 N.A


C - 7

Intersection 7 :


Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 2.6 2.7 N.A 2.7 2.1 3.9Minibuses 1.2 N.A 1.1 N.A 1.2 N.ACar 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.1 0.2 0.2 0.2Bicycle 0.3 0.2 0.2 0.2 0.3 0.2Becak(Rickshaw) 0.7 0.5 0.4 N.A 0.8 N.A

Tricycles N.A N.A N.A N.A N.A N.APushcart(2-wheels) N.A N.A N.A N.A 1.2 N.A

Intersection 8 :


Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle N.A N.A 3.0 3.3 2.2 2.8Minibuses N.A N.A N.A 1.8 N.A 2.8Car 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.1 0.1 0.2 0.1Bicycle 0.3 0.2 0.1 0.2 0.3 0.1Becak(Rickshaw) 0.8 0.3 0.3 N.A 0.6 N.A

Tricycles N.A 0.4 N.A N.A N.A N.APushcart(2-wheels) 1.2 N.A N.A N.A N.A N.A

Intersection 9 :


Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle 2.7 3.1 4.7 3.7 2.3 N.AMinibuses N.A N.A N.A 3.0 N.A N.ACar 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.1 0.1 0.1 0.2 0.1Bicycle 0.3 0.1 0.1 0.1 0.3 0.2Becak(Rickshaw) 0.9 0.6 0.3 0.7 N.A 0.7

Tricycles N.A N.A N.A N.A 0.5 N.APushcart(2-wheels) N.A N.A N.A N.A N.A 0.7


C - 8

Intersection 10 :


Truck 3 axle N.A N.A N.A N.A N.A N.ATruck 2 axle N.A 2.5 N.A 2.3 2.6 3.2Minibuses N.A N.A N.A N.A N.A N.ACar 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.2 0.2 0.2 0.2Bicycle 0.4 0.3 0.2 0.2 0.5 0.2Becak(Rickshaw) 0.9 1.0 N.A N.A 1.0 N.A

Tricycles N.A 0.6 0.5 0.6 0.8 N.APushcart(2-wheels) N.A N.A N.A N.A 2.4 1.1

Average PCUs of Each Type Vehicle from Each Intersection :

Intersection/ PCUsType ofVehicle 1 2 3 4 5 6 7 8 9 10

Truck3 axle 2.7 5.3 N.A N.A N.A 2.5 N.A N.A N.A N.A

Truck2 axle 2.8 2.7 3.3 2.5 3.1 2.8 3.5 2.8 3.3 2.5

Minibuses 1.7 2.1 1.4 1.9 1.6 1.8 1.2 2.3 3.0 N.ACar 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0Motorcycle 0.2 0.2 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.2Bicycle 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3Becak(Rickshaw) 0.7 0.6 0.5 0.7 0.6 0.6 0.6 0.5 0.6 1.0

Tricycles N.A 0.5 1.0 0.3 0.5 0.4 N.A 0.4 0.5 0.6Pushcart(2-wheels) 1.0 1.0 0.9 0.8 1.4 0.9 1.2 1.2 0.7 1.7

APPENDIX D : Matrix of Maximum Flow (Capacity)

D - 1

Appendix D : Matrix of Maximum Flow (Capacity)

D. Matrix of Maximum Flow (Capacity)

Intersection – 1

Data V = 22 km/hSpeed Maximum


VC-A' QC-A(1) 1923.16 0.00 0.00 917.91 0.00 875.93 3716.99

VC-B' QC-B(2) 2347.69 0.00 0.00 0.00 0.00 3484.62 5832.31

VB-C' QB-C(3) 2347.69 982.31 0.00 0.00 0.00 848.75 4178.75

VB-A' QB-A(4) 2343.02 0.00 0.00 10.08 0.00 875.93 3229.04

VA-C' QA-C(5) 1960.13 412.16 0.00 837.96 935.70 864.53 5010.48

VA-B' QA-B(6) 2347.69 415.19 0.00 0.00 0.00 864.44 3627.32

Capacity of Intersection = 3229.04

Model V = 10 km/hSpeed Maximum


10 QC-A(1) 2141.56 0.00 574.16 1022.15 0.00 1667.14 5405.01

10 QC-B(2) 2054.47 458.93 717.28 1243.95 1721.78 4976.66 11173.06

10 QB-C(3) 2226.42 237.84 754.49 806.04 2169.45 1901.58 8095.83

10 QB-A(4) 2505.65 129.89 843.04 94.96 3234.76 1795.18 8603.47

10 QA-C(5) 2466.90 2161.09 712.34 193.62 1662.40 3797.36 10993.70

10 QA-B(6) 2174.83 1520.66 737.71 937.44 1967.60 3166.08 10504.31




11 QC-A(1) 1877.01 0.00 423.02 895.88 0.00 1381.43 4577.34

11 QC-B(2) 1767.17 412.80 558.28 1175.60 1627.18 4931.19 10472.23

11 QB-C(3) 1863.47 309.48 555.89 930.35 1598.39 1686.49 6944.08

11 QB-A(4) 2197.66 96.54 660.61 79.30 2858.22 1476.59 7368.91

11 QA-C(5) 2162.96 1871.46 541.82 167.67 1429.21 3226.15 9399.27

11 QA-B(6) 1905.45 1260.05 554.65 823.44 1583.46 2623.48 8750.53




12 QC-A(1) 1612.46 0.00 271.89 769.61 0.00 1095.71 3749.67

12 QC-B(2) 1479.87 366.67 399.28 1107.26 1532.58 4885.72 9771.39

12 QB-C(3) 1500.53 381.13 357.28 1054.66 1027.33 1471.40 5792.33

12 QB-A(4) 1889.67 63.19 478.18 63.64 2481.69 1158.00 6134.36

12 QA-C(5) 1859.01 1581.83 371.31 141.72 1196.02 2654.95 7804.84

12 QA-B(6) 1636.08 999.44 371.58 709.45 1199.32 2080.87 6996.75



D - 2

Model V = 12.6 km/hSpeed Maximum


12.6 QC-A(1) 1453.72 0.00 181.21 693.85 0.00 924.29 3253.07

12.6 QC-B(2) 1307.49 339.00 303.88 1066.25 1475.82 4858.44 9350.89

12.6 QB-C(3) 1282.76 424.11 238.12 1129.25 684.70 1342.34 5101.28

12.6 QB-A(4) 1704.88 43.18 368.72 54.24 2255.77 966.84 5393.62

12.6 QA-C(5) 1676.64 1408.05 269.00 126.16 1056.11 2312.22 6848.18

12.6 QA-B(6) 1474.45 843.07 261.74 641.06 968.84 1755.31 5944.48




13 QC-A(1) 1347.90 0.00 120.76 643.34 0.00 810.00 2922.00

13 QC-B(2) 1192.57 320.55 240.29 1038.91 1437.98 4840.25 9070.55

13 QB-C(3) 1137.58 452.77 158.68 1178.97 456.28 1256.30 4640.58

13 QB-A(4) 1581.68 29.83 295.74 47.98 2105.15 839.41 4899.80

13 QA-C(5) 1555.06 1292.20 200.79 115.78 962.83 2083.74 6210.40

13 QA-B(6) 1366.70 738.83 188.52 595.46 815.18 1538.27 5242.97




14 QC-A(1) 1083.35 0.00 0.00 517.07 0.00 524.29 2124.71

14 QC-B(2) 905.27 274.42 81.29 970.57 1343.38 4794.78 8369.71

14 QB-C(3) 806.28 479.23 0.00 1222.68 0.00 996.67 3504.86

14 QB-A(4) 1273.70 0.00 113.36 32.32 1729.26 524.29 3672.92

14 QA-C(5) 1251.12 1002.57 30.27 89.83 729.64 1512.54 4615.97

14 QA-B(6) 1097.33 478.22 5.45 481.47 431.04 995.67 3489.18




15 QC-A(1) 818.79 0.00 0.00 390.80 0.00 238.57 1448.17

15 QC-B(2) 617.98 228.29 0.00 902.22 1248.78 4749.31 7746.58

15 QB-C(3) 600.79 326.07 0.00 945.99 0.00 559.99 2432.84

15 QB-A(4) 965.71 0.00 0.00 16.66 1358.76 238.57 2579.70

15 QA-C(5) 948.11 686.10 0.00 61.47 496.46 914.87 3107.02

15 QA-B(6) 827.95 217.61 0.00 367.48 46.90 453.07 1913.02



D - 3

Intersection – 2



VC-A' QC-A(1) 307.91 2196.59 0.00 207.65 4186.61 0.00 6898.77

VC-B' QC-B(2) 0.00 0.00 0.00 3367.55 3785.87 15534.17 22687.59

VB-C' QB-C(3) 0.00 0.00 0.00 1775.89 0.00 4700.83 6476.72

VB-A' QB-A(4) 640.15 2127.60 0.00 0.00 0.00 76.25 2844.01

VA-C' QA-C(5) 640.15 0.00 0.00 0.00 0.00 4700.83 5340.99

VA-B' QA-B(6) 640.15 1405.76 0.00 0.00 7285.35 1645.25 10976.52




10 QC-A(1) 2010.52 0.00 574.16 1355.88 0.00 1667.14 5607.70

10 QC-B(2) 1928.08 734.84 695.67 1565.82 1461.80 5248.63 11634.84

10 QB-C(3) 1988.53 714.33 684.83 1411.87 1331.49 2371.27 8502.32

10 QB-A(4) 2501.87 539.09 847.98 104.56 3294.22 2198.53 9486.27

10 QA-C(5) 2445.42 2764.76 686.10 248.33 1346.69 4392.40 11883.69

10 QA-B(6) 1912.60 9028.90 1534.79 1605.25 11556.70 10567.06 36205.30




11 QC-A(1) 1762.15 0.00 423.02 1188.38 0.00 1381.43 4754.99

11 QC-B(2) 1648.02 660.98 537.80 1479.05 1380.80 5175.82 10882.47

11 QB-C(3) 1686.47 697.79 504.57 1381.13 981.00 2069.25 7320.20

11 QB-A(4) 2194.51 400.04 664.16 87.32 2900.96 1775.76 8022.75

11 QA-C(5) 2144.49 2390.44 519.26 214.71 1157.78 3737.72 10164.40

11 QA-B(6) 1683.66 7481.53 1214.72 1388.28 9524.36 8756.08 30048.63




12 QC-A(1) 1513.79 0.00 271.89 1020.89 0.00 1095.71 3902.28

12 QC-B(2) 1367.95 587.12 379.93 1392.29 1299.80 5103.02 10130.11

12 QB-C(3) 1384.40 681.24 324.30 1350.39 630.52 1767.23 6138.08

12 QB-A(4) 1887.14 260.99 480.34 70.08 2507.70 1352.98 6559.22

12 QA-C(5) 1843.55 2016.13 352.43 181.09 968.88 3083.04 8445.12

12 QA-B(6) 1454.72 5934.16 894.65 1171.31 7492.02 6945.10 23891.97



D - 4



13 QC-A(1) 1265.42 0.00 120.76 853.39 0.00 810.00 3049.57

13 QC-B(2) 1087.88 513.26 222.07 1305.53 1218.80 5030.21 9377.75

13 QB-C(3) 1082.34 664.70 144.03 1319.65 280.04 1465.20 4955.96

13 QB-A(4) 1579.78 121.94 296.51 52.83 2114.43 930.20 5095.70

13 QA-C(5) 1542.62 1641.82 185.59 147.47 779.98 2428.36 6725.83

13 QA-B(6) 1225.78 4386.79 574.58 954.34 5459.68 5134.12 17735.31




13.1 QC-A(1) 1240.59 0.00 105.64 836.64 0.00 781.43 2964.30

13.1 QC-B(2) 1059.87 505.87 206.28 1296.85 1210.70 5022.93 9302.52

13.1 QB-C(3) 1052.13 663.04 126.01 1316.58 244.99 1435.00 4837.75

13.1 QB-A(4) 1549.04 108.04 278.13 51.11 2075.11 887.92 4949.35

13.1 QA-C(5) 1512.52 1604.38 168.91 144.10 761.09 2362.89 6553.90

13.1 QA-B(6) 1202.89 4232.06 542.58 932.65 5256.45 4953.03 17119.64




14 QC-A(1) 1017.06 0.00 0.00 685.90 0.00 524.29 2227.24

14 QC-B(2) 807.81 439.40 64.20 1218.77 1137.81 4957.41 8625.39

14 QB-C(3) 800.08 612.16 0.00 1238.46 0.00 1127.70 3778.40

14 QB-A(4) 1272.41 0.00 112.95 35.59 1724.27 524.29 3669.50

14 QA-C(5) 1241.68 1267.50 18.75 113.85 591.08 1773.68 5006.54

14 QA-B(6) 996.84 2839.42 254.51 737.38 3427.34 3323.15 11578.64




15 QC-A(1) 768.69 0.00 0.00 518.40 0.00 238.57 1525.66

15 QC-B(2) 527.75 365.54 0.00 1132.00 1056.81 4884.60 7966.70

15 QB-C(3) 596.08 416.52 0.00 956.73 0.00 649.14 2618.96

15 QB-A(4) 965.05 0.00 0.00 18.35 1356.19 238.57 2578.16

15 QA-C(5) 941.75 864.86 0.00 77.68 402.17 1091.08 3377.54

15 QA-B(6) 767.90 1292.05 0.00 520.41 1395.00 1512.17 5487.53



D - 5

Intersection – 3



VC-A' QC-A(1) 2975.72 0.00 0.00 129.40 1946.40 0.00 5051.52

VC-B' QC-B(2) 2839.88 263.23 0.00 72.47 469.00 0.00 3644.57

VB-C' QB-C(3) 3420.47 346.26 20.48 315.81 88.64 0.00 4191.65

VB-A' QB-A(4) 3328.83 0.00 27.54 277.40 0.00 0.00 3633.77

VA-C' QA-C(5) 2853.78 245.51 0.00 78.29 663.77 0.00 3841.35

VA-B' QA-B(6) 2819.13 0.00 0.00 63.77 1422.85 0.00 4305.75




10 QC-A(1) 2500.24 165.70 618.62 108.73 534.90 1830.47 5758.65

10 QC-B(2) 2393.57 659.78 783.61 380.38 2519.78 5174.64 11911.76

10 QB-C(3) 2542.93 0.00 896.79 0.00 3881.34 1667.14 8988.20

10 QB-A(4) 2517.23 175.21 847.46 65.45 3287.98 1839.85 8733.18

10 QA-C(5) 2529.54 1210.45 761.99 34.11 2259.67 2860.30 9656.05

10 QA-B(6) 2464.45 0.00 597.33 199.86 278.76 0.00 3540.40




10.3 QC-A(1) 2407.58 152.55 569.75 104.70 492.47 1731.80 5458.85

10.3 QC-B(2) 2301.80 639.89 734.80 374.07 2477.99 5155.03 11683.58

10.3 QB-C(3) 2448.69 0.00 825.97 0.00 3574.83 1581.43 8430.92

10.3 QB-A(4) 2424.26 161.89 792.50 62.21 3172.20 1741.01 8354.06

10.3 QA-C(5) 2435.81 1164.34 708.74 32.81 2164.58 2729.13 9235.42

10.3 QA-B(6) 2372.58 0.00 549.97 193.83 254.45 0.00 3370.83




11 QC-A(1) 2191.38 121.89 455.73 95.29 393.46 1501.57 4759.32

11 QC-B(2) 2087.69 593.47 620.90 359.35 2380.48 5109.27 11151.15

11 QB-C(3) 2228.80 0.00 660.73 0.00 2859.66 1381.43 7130.61

11 QB-A(4) 2207.33 130.81 664.25 54.66 2902.03 1510.37 7469.45

11 QA-C(5) 2217.10 1056.75 584.51 29.78 1942.70 2423.09 8253.93

11 QA-B(6) 2158.21 0.00 439.46 179.77 197.72 0.00 2975.15



D - 6



12 QC-A(1) 1882.51 78.07 292.84 81.86 252.03 1172.67 3759.99

12 QC-B(2) 1781.81 527.15 458.18 338.33 2241.17 5043.90 10390.54

12 QB-C(3) 1716.15 231.40 424.67 505.53 183799 1323.81 6039.55

12 QB-A(4) 1897.44 86.42 481.03 43.86 2516.07 1180.90 6205.72

12 QA-C(5) 1904.67 903.06 407.03 25.45 1625.73 1985.87 6851.81

12 QA-B(6) 1851.96 0.00 281.59 159.68 116.68 0.00 2409.91




13 QC-A(1) 1573.65 34.26 129.95 68.43 110.60 843.77 2760.66

13 QC-B(2) 1475.93 460.83 295.47 317.30 2101.87 4978.54 9629.94

13 QB-C(3) 1203.01 1036.98 188.61 1012.34 816.32 1832.17 6089.43

13 QB-A(4) 1587.54 42.03 297.82 33.07 2130.11 851.43 4941.99

13 QA-C(5) 1592.23 749.37 229.54 21.12 1308.77 1548.66 5449.69

13 QA-B(6) 1545.71 0.00 123.72 139.59 35.64 0.00 1844.66




14 QC-A(1) 1264.79 0.00 0.00 55.00 0.00 524.29 1844.08

14 QC-B(2) 1170.05 394.52 132.76 296.27 1962.57 4913.17 8869.33

14 QB-C(3) 755.00 1579.20 0.00 1353.28 0.00 2080.93 5768.40

14 QB-A(4) 1277.64 0.00 114.64 22.28 1744.58 524.29 3683.42

14 QA-C(5) 1279.80 595.67 52.06 16.79 991.80 1111.45 4047.57

14 QA-B(6) 1239.46 0.00 0.00 119.50 0.00 0.00 1358.97




15 QC-A(1) 955.93 0.00 0.00 41.57 0.00 238.57 1236.07

15 QC-B(2) 864.17 328.20 0.00 275.24 1823.26 4847.80 8138.67

15 QB-C(3) 565.90 1074.50 0.00 1034.85 0.00 1297.72 3972.97

15 QB-A(4) 967.74 0.00 0.00 11.48 1366.66 238.57 2584.46

15 QA-C(5) 967.68 413.38 0.00 11.65 674.83 646.05 2713.58

15 QA-B(6) 933.21 0.00 0.00 99.42 0.00 0.00 1032.63



D - 7

Intersection – 4



VC-A' QC-A(1) 912.21 0.00 136.27 1024.39 0.00 763.30 2836.18

VC-B' QC-B(2) 2227.27 83.27 136.27 0.00 0.00 1848.51 4295.32

VB-C' QB-C(3) 2227.27 252.57 71.63 0.00 383.94 815.61 3751.03

VB-A' QB-A(4) 2206.65 0.00 0.00 16.06 1020.05 763.30 4006.07

VA-C' QA-C(5) 1268.74 70.62 21.04 746.67 684.42 777.93 3569.42

VA-B' QA-B(6) 2050.80 160.63 136.27 137.47 0.00 796.57 3281.74




10 QC-A(1) 1764.75 0.00 574.16 1981.77 0.00 1667.14 5987.82

10 QC-B(2) 1877.37 272.60 681.91 1694.96 1296.31 4793.00 10616.16

10 QB-C(3) 2542.93 0.00 1035.54 0.00 5550.64 1667.14 10796.26

10 QB-A(4) 2503.71 81.18 841.68 99.90 3218.40 1747.16 8492.02

10 QA-C(5) 2329.78 1795.88 684.86 542.83 1331.77 3437.37 10122.49

10 QA-B(6) 2065.57 419.58 607.67 1215.68 403.20 2080.73 6792.43




11 QC-A(1) 1546.74 0.00 423.02 1736.96 0.00 1381.43 5088.15

11 QC-B(2) 1599.59 245.20 524.89 1602.38 1225.51 4765.99 9963.56

11 QB-C(3) 2228.80 0.00 762.96 0.00 4089.56 1381.43 8462.75

11 QB-A(4) 2196.04 60.29 659.54 83.42 2845.36 1440.86 7285.50

11 QA-C(5) 2044.51 1552.65 518.20 469.31 1144.96 2911.90 8641.53

11 QA-B(6) 1808.10 347.67 446.29 1071.37 279.86 1724.14 5677.43




12 QC-A(1) 1328.74 0.00 271.89 1492.14 0.00 1095.71 4188.49

12 QC-B(2) 1321.81 217.80 367.87 1509.80 1154.70 4738.98 9310.96

12 QB-C(3) 1914.66 0.00 490.38 0.00 2628.48 1095.71 6129.23

12 QB-A(4) 1888.37 39.39 477.40 66.95 2472.33 1134.55 6078.98

12 QA-C(5) 1759.24 1309.42 351.53 395.79 958.15 2386.43 7160.57

12 QA-B(6) 1550.63 275.77 284.90 927.07 156.53 1367.54 4562.43



D - 8



13 QC-A(1) 1110.73 0.00 120.76 1247.33 0.00 810.00 3288.82

13 QC-B(2) 1044.02 190.40 210.85 1417.22 1083.90 4711.97 8658.37

13 QB-C(3) 1338.03 79.07 217.79 668.49 1167.40 887.94 4358.72

13 QB-A(4) 1580.70 18.50 295.26 50.48 2099.29 828.24 4872.47

13 QA-C(5) 1473.98 1066.19 184.87 322.27 771.34 1860.96 5679.62

13 QA-B(6) 1293.16 203.86 123.51 782.76 33.19 1010.95 3447.43




13.6 QC-A(1) 979.93 0.00 30.08 1100.44 0.00 638.57 2749.02

13.6 QC-B(2) 877.35 173.96 116.64 1361.67 1041.41 4695.76 8266.81

13.6 QB-C(3) 979.75 187.90 54.24 1100.91 290.75 823.79 3437.34

13.6 QB-A(4) 1396.10 5.97 185.97 40.59 1875.47 644.45 4148.55

13.6 QA-C(5) 1302.82 920.26 84.87 278.16 659.25 1545.68 4791.04

13.6 QA-B(6) 1138.68 160.71 30.08 696.17 0.00 796.99 2822.63




14 QC-A(1) 892.73 0.00 0.00 1002.52 0.00 524.29 2419.53

14 QC-B(2) 766.24 163.00 53.83 1324.64 1013.09 4684.96 8005.77

14 QB-C(3) 818.81 210.47 0.00 1190.77 0.00 731.75 2951.80

14 QB-A(4) 1273.04 0.00 113.15 34.00 1726.69 524.29 3671.16

14 QA-C(5) 1188.71 822.96 18.21 248.75 584.53 1335.49 4198.66

14 QA-B(6) 1035.69 131.95 0.00 638.45 0.00 654.35 2460.44




15 QC-A(1) 674.72 0.00 0.00 757.70 0.00 238.57 1671.00

15 QC-B(2) 488.46 135.60 0.00 1232.06 942.28 4657.95 7456.36

15 QB-C(3) 609.31 143.21 0.00 924.28 0.00 379.73 2056.54

15 QB-A(4) 965.37 0.00 0.00 17.53 1357.44 238.57 2578.91

15 QA-C(5) 905.61 561.48 0.00 169.71 397.72 792.03 2826.54

15 QA-B(6) 778.22 60.04 0.00 494.14 0.00 297.76 1630.16



D - 9

Intersection – 5



VC-A' QC-A(1) 2398.96 153.69 0.00 146.78 398.93 224.86 3323.20

VC-B' QC-B(2) 2123.99 146.99 0.00 224.77 817.29 1789.15 5102.19

VB-C' QB-C(3) 2916.43 0.00 0.00 0.00 5011.43 301.07 8228.93

VB-A' QB-A(4) 2916.43 0.00 0.00 0.00 1759.89 301.07 4977.39

VA-C' QA-C(5) 2321.01 544.78 0.00 168.89 884.36 30.90 3949.93

VA-B' QA-B(6) 2916.43 456.98 0.00 0.00 0.00 74.44 3447.85




10 QC-A(1) 2483.27 233.30 624.50 151.94 605.59 1897.11 5995.71

10 QC-B(2) 2425.99 828.77 791.28 297.80 2612.08 5341.22 12297.15

10 QB-C(3) 2053.22 1632.51 719.55 1247.14 1749.12 3276.33 10677.87

10 QB-A(4) 2518.25 262.54 849.11 62.85 3307.77 1925.93 8926.45

10 QA-C(5) 2536.58 1353.28 759.28 16.18 2227.10 3001.09 9893.51

10 QA-B(6) 2481.88 0.00 598.88 155.49 297.42 0.00 3533.66




10.6 QC-A(1) 2299.21 195.09 525.57 140.68 506.41 1688.02 5354.97

10.6 QC-B(2) 2241.41 778.79 693.36 287.88 2524.99 5291.95 11818.38

10.6 QB-C(3) 1851.20 1704.21 605.91 1281.62 1472.87 3175.58 10091.38

10.6 QB-A(4) 2332.21 222.67 738.95 56.63 3073.41 1715.21 8139.08

10.6 QA-C(5) 2348.59 1249.56 653.02 14.94 2039.66 2727.42 9033.18

10.6 QA-B(6) 2297.08 0.00 504.06 146.11 247.67 0.00 3194.92




11 QC-A(1) 2176.50 169.62 459.62 133.17 440.28 1548.62 4927.81

11 QC-B(2) 2118.36 745.47 628.08 281.26 2466.93 5259.10 11499.20

11 QB-C(3) 1716.52 1752.01 530.14 1304.61 1288.70 3108.41 9700.39

11 QB-A(4) 2208.19 196.09 665.51 52.48 2917.16 1574.72 7614.16

11 QA-C(5) 2223.26 1180.41 582.18 14.11 1914.70 2544.97 8459.63

11 QA-B(6) 2173.88 0.00 440.85 139.86 214.50 0.00 2969.09



D - 10



12 QC-A(1) 1869.74 105.93 294.75 114.40 274.97 1200.13 3859.91

12 QC-B(2) 1810.72 662.17 464.88 264.71 2321.79 5176.99 10701.26

12 QB-C(3) 1379.81 1871.51 340.74 1362.08 828.29 2940.49 8722.91

12 QB-A(4) 1898.12 129.65 481.91 42.12 2526.56 1223.51 6301.87

12 QA-C(5) 1909.93 1007.54 405.08 12.04 1602.30 2088.86 7025.75

12 QA-B(6) 1865.88 0.00 282.83 124.23 131.59 0.00 2404.52




13 QC-A(1) 1562.97 42.24 129.87 95.63 109.66 851.64 2792.01

13 QC-B(2) 1503.08 578.86 301.69 248.16 2176.64 5094.88 9903.31

13 QB-C(3) 1043.11 1991.01 151.33 1419.55 367.87 2772.56 7745.43

13 QB-A(4) 1588.05 63.21 298.30 31.76 2135.95 872.30 4989.57

13 QA-C(5) 1596.61 834.67 227.98 9.98 1289.90 1632.74 5591.87

13 QA-B(6) 1557.88 0.00 124.80 108.60 48.67 0.00 1839.95




14 QC-A(1) 1256.21 0.00 0.00 76.86 0.00 524.29 1857.35

14 QC-B(2) 1195.44 495.56 138.49 231.61 2031.50 5012.77 9105.37

14 QB-C(3) 737.22 1960.54 0.00 1398.55 0.00 2456.82 6553.14

14 QB-A(4) 1277.99 0.00 114.75 21.39 1745.93 524.29 3684.34

14 QA-C(5) 1283.28 661.80 50.88 7.91 977.50 1176.63 4157.99

14 QA-B(6) 1249.88 0.00 0.00 92.97 0.00 0.00 1342.85




15 QC-A(1) 949.44 0.00 0.00 58.09 0.00 238.57 1246.10

15 QC-B(2) 887.80 412.26 0.00 215.06 1886.35 4930.66 8332.13

15 QB-C(3) 553.80 1333.97 0.00 1065.66 0.00 1553.49 4506.92

15 QB-A(4) 967.92 0.00 0.00 11.03 1367.36 238.57 2584.88

15 QA-C(5) 970.10 458.61 0.00 5.48 665.10 690.63 2789.91

15 QA-B(6) 941.88 0.00 0.00 77.35 0.00 0.00 1019.23



D - 11

Intersection – 6



VC-A' QC-A(1) 926.69 13.64 535.15 549.44 113.49 1232.21 3370.62

VC-B' QC-B(2) 1504.31 88.17 592.49 0.00 0.00 5700.31 7885.28

VB-C' QB-C(3) 1504.31 33.60 96.83 0.00 981.09 1211.49 3827.31

VB-A' QB-A(4) 1445.53 29.51 171.89 55.91 832.51 1215.74 3751.09

VA-C' QA-C(5) 1024.70 0.00 329.07 456.21 521.39 1246.36 3577.74

VA-B' QA-B(6) 1163.42 157.63 463.51 324.26 255.30 1082.76 3446.88




10 QC-A(1) 2062.70 0.00 574.16 1223.00 0.00 1667.14 5527.00

10 QC-B(2) 1940.00 191.41 693.11 1535.46 1431.08 4712.96 10504.02

10 QB-C(3) 2542.93 0.00 3639.10 0.00 36872.20 1667.14 44721.38

10 QB-A(4) 2504.96 45.98 841.56 96.70 3216.90 1712.46 8418.55

10 QA-C(5) 2312.42 1252.04 697.90 587.03 1488.65 2901.30 9239.33

10 QA-B(6) 2112.28 283.38 596.95 1096.73 274.17 1946.47 6309.98




11 QC-A(1) 1807.88 0.00 423.02 1071.92 0.00 1381.43 4684.26

11 QC-B(2) 1658.72 172.17 535.50 1451.80 1353.12 4693.99 9865.30

11 QB-C(3) 2228.80 0.00 2681.19 0.00 27166.44 1381.43 33457.85

11 QB-A(4) 2197.09 34.16 659.48 80.75 2844.71 1415.10 7231.29

11 QA-C(5) 2029.37 1083.20 529.41 507.86 1279.83 2449.15 7878.82

11 QA-B(6) 1849.10 234.81 437.60 966.96 175.41 1612.89 5276.77




12 QC-A(1) 1553.07 0.00 271.89 920.84 0.00 1095.71 3841.52

12 QC-B(2) 1377.43 152.93 377.88 1368.15 1275.15 4675.03 9226.58

12 QB-C(3) 1914.66 0.00 1723.28 0.00 17460.67 1095.71 22194.33

12 QB-A(4) 1889.21 22.35 477.41 64.80 2472.51 1117.74 6044.03

12 QA-C(5) 1746.32 914.35 360.92 428.70 1071.02 1997.01 6518.31

12 QA-B(6) 1585.92 186.25 278.26 837.19 76.64 1279.30 4243.57



D - 12



12.8 QC-A(1) 1349.22 0.00 150.98 799.97 0.00 867.14 3167.32

12.8 QC-B(2) 1152.40 137.54 251.79 1301.23 1212.78 4659.86 8715.60

12.8 QB-C(3) 1663.35 0.00 956.95 0.00 9696.06 867.14 13183.51

12.8 QB-A(4) 1642.91 12.89 331.76 52.05 2174.75 879.85 5094.22

12.8 QA-C(5) 1519.88 779.28 226.12 365.37 903.96 1635.29 5429.90

12.8 QA-B(6) 1375.38 147.40 150.98 733.38 0.00 1012.43 3419.56




13 QC-A(1) 1298.26 0.00 120.76 769.76 0.00 810.00 2998.78

13 QC-B(2) 1096.14 133.69 220.27 1284.50 1197.19 4656.07 8587.85

13 QB-C(3) 1600.52 0.00 765.37 0.00 7754.91 810.00 10930.80

13 QB-A(4) 1581.34 10.53 295.34 48.86 2100.32 820.38 4856.77

13 QA-C(5) 1463.27 745.51 192.42 349.54 862.20 1544.86 5157.80

13 QA-B(6) 1322.74 137.68 120.76 707.42 0.00 945.72 3234.32




14 QC-A(1) 1043.45 0.00 0.00 618.68 0.00 524.29 2186.41

14 QC-B(2) 814.85 114.45 62.66 1200.85 1119.22 4637.10 7949.13

14 QB-C(3) 820.93 164.86 0.00 1185.36 0.00 686.79 2857.94

14 QB-A(4) 1273.46 0.00 113.29 32.91 1728.35 524.29 3672.30

14 QA-C(5) 1180.22 576.67 23.93 270.37 653.38 1092.71 3797.29

14 QA-B(6) 1059.56 89.12 0.00 577.65 0.00 612.13 2338.46




15 QC-A(1) 788.64 0.00 0.00 467.60 0.00 238.57 1494.81

15 QC-B(2) 533.56 95.21 0.00 1117.20 1041.26 4618.14 7405.37

15 QB-C(3) 0.00 112.17 0.00 920.60 0.00 349.14 1381.91

15 QB-A(4) 965.59 0.00 0.00 16.97 1358.30 238.57 2579.42

15 QA-C(5) 899.73 393.93 0.00 184.70 444.57 626.88 2549.80

15 QA-B(6) 796.38 40.55 0.00 447.88 0.00 278.54 1563.36



D - 13

Intersection – 7



VC-A' QC-A(1) 2395.42 0.00 0.00 754.78 0.00 288.33 3438.53

VC-B' QC-B(2) 2851.93 525.97 0.00 433.69 1478.27 1363.19 6653.05

VB-C' QB-C(3) 3468.52 0.00 0.00 0.00 9831.43 288.33 13588.28

VB-A' QB-A(4) 3386.13 0.00 0.00 57.95 1273.60 288.33 5006.01

VA-C' QA-C(5) 2639.41 579.28 0.00 583.17 606.57 276.09 4684.53

VA-B' QA-B(6) 3098.55 661.93 0.00 260.23 0.00 274.35 4295.05




10 QC-A(1) 2262.94 5740.18 1206.62 713.04 7608.69 7325.32 24856.79

10 QC-B(2) 2058.30 1140.89 724.06 1234.20 1803.36 5648.88 12609.68

10 QB-C(3) 1878.90 1536.93 661.81 1691.06 1054.55 3182.12 10005.38

10 QB-A(4) 2499.91 28863.90 1273.83 109.56 8417.23 30118.71 71283.14

10 QA-C(5) 2491.30 2795.93 698.31 131.50 1493.64 4423.13 12033.81

10 QA-B(6) 2292.79 0.00 582.05 637.02 94.95 0.00 3606.81




10.6 QC-A(1) 2095.21 2872.22 799.94 660.19 3807.16 4326.90 14561.62

10.6 QC-B(2) 1886.13 1072.08 628.33 1192.67 1742.68 5581.05 12102.94

10.6 QB-C(3) 1711.10 1480.24 557.29 1638.39 888.00 2954.81 9229.83

10.6 QB-A(4) 2315.69 24386.23 1097.43 98.72 7386.07 25533.57 60817.71

10.6 QA-C(5) 2306.,99 2569.74 597.18 120.86 1367.93 4028.75 10991.45

10.6 QA-B(6) 2119.40 0.00 488.25 598.60 57.41 0.00 3263.66




11 QC-A(1) 1983.40 960.24 528.82 624.95 1272.82 2327.95 7698.18

11 QC-B(2) 1771.34 1026.21 564.52 1164.98 1702.22 5535.84 11765.12

11 QB-C(3) 1599.24 1442.44 487.61 1603.28 776.96 2803.27 8712.80

11 QB-A(4) 2192.87 21401.12 979.84 91.49 6698.63 22476.81 53840.76

11 QA-C(5) 2184.12 2418.95 529.76 113.77 1284.12 3765.82 10296.54

11 QA-B(6) 2003.80 0.00 425.71 572.99 32.38 0.00 3034.89



D - 14



12 QC-A(1) 1703.85 0.00 271.89 536.87 0.00 1095.71 3608.32

12 QC-B(2) 1484.39 911.54 404.98 1095.77 1601.09 5422.80 10920.56

12 QB-C(3) 1319.57 1347.96 313.40 1515.51 499.38 2424.41 7420.22

12 QB-A(4) 1885.83 13938.33 685.85 73.43 4980.04 14834.92 36398.39

12 QA-C(5) 1876.95 2041.96 361.21 96.04 1074.61 3108.50 8559.27

12 QA-B(6) 1714.80 0.00 271.89 508.96 0.00 0.00 2495.66




13 QC-A(1) 1424.30 0.00 120.76 448.79 0.00 810.00 2803.84

13 QC-B(2) 1197.43 796.87 245.44 1026.55 1499.95 5309.77 10076.01

13 QB-C(3) 1039.90 1253.47 139.19 1427.73 221.79 2045.56 6127.64

13 QB-A(4) 1578.79 6475.54 391.86 55.36 3261.44 7193.03 18956.02

13 QA-C(5) 1569.77 1664.98 192.66 78.31 865.09 2451.19 6822.00

13 QA-B(6) 1425.81 0.00 120.76 444.94 0.00 0.00 1991.50




14 QC-A(1) 1144.75 0.00 0.00 360.70 0.00 524.29 2029.74

14 QC-B(2) 910.47 682.19 85.90 957.33 1398.82 5196.73 9231.45

14 QB-C(3) 777.02 1106.87 0.00 1297.20 0.00 1615.34 4796.42

14 QB-A(4) 1271.74 0.00 112.73 37.29 1721.67 524.29 3667.73

14 QA-C(5) 1262.60 1287.99 24.12 60.58 655.57 1793.87 5084.73

14 QA-B(6) 1136.82 0.00 0.00 380.91 0.00 0.00 1517.72




15 QC-A(1) 865.20 0.00 0.00 272.62 0.00 238.57 1376.39

15 QC-B(2) 623.51 567.52 0.00 888.12 1297.68 5083.70 8460.53

15 QB-C(3) 580.88 753.12 0.00 996.70 0.00 980.93 3311.63

15 QB-A(4) 964.70 0.00 0.00 19.22 1354.85 238.57 2577.35

15 QA-C(5) 956.00 879.89 0.00 41.38 446.06 1105.90 3429.23

15 QA-B(6) 847.82 0.00 0.00 316.88 0.00 0.00 1164.70



D - 15

Intersection – 8



VC-A' QC-A(1) 1795.28 214.71 441.64 1512.23 184.08 483.69 4631.62

VC-B' QC-B(2) 1300.00 431.90 368.65 238.92 327.05 504.66 3171.17

VB-C' QB-C(3) 1207.06 3491.25 421.19 0.00 224.14 0.00 5343.64

VB-A' QB-A(4) 1207.06 735.76 312.07 0.00 437.85 128.33 2821.08

VA-C' QA-C(5) 1207.06 905.17 284.35 0.00 492.16 12.79 2901.53

VA-B' QA-B(6) 1333.51 445.15 49.45 325.07 952.24 326.53 3431.94




10 QC-A(1) 1910.89 686.76 623.10 1609.61 588.78 2344.09 7763.22

10 QC-B(2) 1800.95 1286.59 676.36 1889.58 1229.57 5792.50 12675.55

10 QB-C(3) 1682.49 1692.96 600.73 2191.25 319.69 3335.92 9823.04

10 QB-A(4) 2497.08 0.00 838.32 116.78 3177.93 1667.14 8297.25

10 QA-C(5) 2487.17 4025.83 656.30 142.00 988.17 5635.46 13934.92

10 QA-B(6) 2162.00 0.00 574.16 970.11 0.00 0.00 3706.26




10.5 QC-A(1) 1792.86 693.19 547.99 1510.19 594.30 2207.57 7346.09

10.5 QC-B(2) 1664.78 1221.93 597.92 1836.37 1194.95 5728.76 12244.71

10.5 QB-C(3) 1570.14 1590.11 521.67 2077.37 277.61 3091.68 9128.58

10.5 QB-A(4) 2343.79 0.00 747.58 107.15 2995.43 1524.29 7718.24

10.5 QA-C(5) 2333.91 3751.35 574.97 132.31 918.86 5222.04 12933.44

10.5 QA-B(6) 2024.08 0.00 498.59 921.36 0.00 0.00 3444.02




11 QC-A(1) 1674.83 699.62 472.88 1410.77 599.81 2071.06 6928.97

11 QC-B(2) 1528.60 1157.28 519.47 1783.16 1160.32 5665.03 11813.86

11 QB-C(3) 1457.79 1487.26 442.60 1963.49 235.54 2847.44 8434.12

11 QB-A(4) 2190.50 0.00 656.84 97.52 2812.93 1381.43 7139.23

11 QA-C(5) 2180.64 3476.87 493.64 122.63 849.56 4808.62 11931.96

11 QA-B(6) 1886.15 0.00 423.02 872.60 0.00 0.00 3181.78



D - 16



12 QC-A(1) 1438.77 712.49 322.66 1211.93 610.84 1798.02 6094.71

12 QC-B(2) 1256.25 1027.96 362.58 1676.74 1091.08 5537.56 10952.17

12 QB-C(3) 1233.10 1281.56 284.47 1735.72 151.39 2358.96 7045.19

12 QB-A(4) 1883.93 0.00 475.37 78.26 2447.93 1095.71 5981.20

12 QA-C(5) 1874.11 2927.91 330.99 103.27 710.94 3981.79 9929.00

12 QA-B(6) 1610.30 0.00 271.89 775.09 0.00 0.00 2657.29




13 QC-A(1) 1202.71 725.35 172.45 1013.09 621.87 1524.99 5260.46

13 QC-B(2) 983.90 898.64 205.69 1570.33 1021.83 5410.09 10090.48

13 QB-C(3) 1008.40 1075.86 126.34 1507.95 67.24 1870.49 5656.27

13 QB-A(4) 1577.35 0.00 293.90 59.01 2082.93 810.00 4823.18

13 QA-C(5) 1567.58 2378.94 168.33 83.91 572.33 3154.96 7926.05

13 QA-B(6) 1334.46 0.00 120.76 677.58 0.00 0.00 2132.80




14 QC-A(1) 966.66 738.22 22.23 814.25 632.90 1251.96 4426.21

14 QC-B(2) 711.55 769.32 48.80 1463.91 952.58 5282.61 9228.79

14 QB-C(3) 788.69 856.48 0.00 1267.47 0.00 1368.53 4281.17

14 QB-A(4) 1270.78 25.95 112.81 39.75 1722.62 549.87 3721.79

14 QA-C(5) 1261.04 1829.98 5.67 64.55 433.72 2328.13 5923.09

14 QA-B(6) 1058.61 0.00 0.00 580.08 0.00 0.00 1638.69




15 QC-A(1) 730.60 751.08 0.00 615.41 643.93 978.92 3719.94

15 QC-B(2) 439.20 640.00 0.00 1357.49 883.34 5155.14 8475.18

15 QB-C(3) 588.82 582.76 0.00 976.47 0.00 813.00 2961.05

15 QB-A(4) 964.21 203.97 0.00 20.49 1389.87 439.63 3018.16

15 QA-C(5) 954.99 1246.00 0.00 43.95 295.11 1466.77 4006.82

15 QA-B(6) 782.76 0.00 0.00 482.57 0.00 0.00 1265.33



D - 17

Intersection – 9



VC-A' QC-A(1) 3230.00 0.00 217.85 1242.16 849.88 0.00 5539.89

VC-B' QC-B(2) 2128.54 214.52 285.73 0.00 0.00 1108.67 3737.46

VB-C' QB-C(3) 2128.54 32.83 245.57 0.00 502.83 0.00 2909.77

VB-A' QB-A(4) 2129.38 0.00 233.06 0.94 659.39 0.00 3022.77

VA-C' QA-C(5) 2302.65 36.43 260.21 196.34 319.50 0.00 3115.13

VA-B' QA-B(6) 2690.02 0.00 153.55 633.20 1654.92 0.00 5131.70




10 QC-A(1) 2209.31 12.76 576.01 849.63 22.32 1679.72 5349.75

10 QC-B(2) 1895.06 745.41 689.47 1649.92 1387.30 5259.05 11626.21

10 QB-C(3) 2012.92 662.18 691.93 1349.76 1416.80 2319.86 8453.45

10 QB-A(4) 2501.32 730.81 850.69 105.96 3326.81 2387.52 9903.12

10 QA-C(5) 2428.16 2782.93 680.51 292.28 1279.45 4410.32 11873.66

10 QA-B(6) 1026.62 44407.96 5321.77 3861.54 57115.20 45440.71 157173.80




11 QC-A(1) 1936.38 0.00 423.02 744.67 0.00 1381.43 4485.51

11 QC-B(2) 1616.83 670.49 531.95 1558.46 1310.41 5185.20 10873.34

11 QB-C(3) 1704.56 656.72 509.79 1335.05 1043.86 2028.77 7278.76

11 QB-A(4) 2194.05 542.19 666.15 88.49 2924.92 1915.87 8331.67

11 QA-C(5) 2129.59 2405.55 514.46 252.65 1099.98 3752.62 10154.85

11 QA-B(6) 948.30 36797.35 4352.58 3260.99 47273.76 37653.10 130286.08




12 QC-A(1) 1663.46 0.00 271.89 639.72 0.00 1095.71 3670.78

12 QC-B(2) 1338.61 595.56 374.42 1467.01 1233.51 5111.34 10120.46

12 QB-C(3) 1396.20 651.27 327.66 1320.35 670.92 1737.68 6104.07

12 QB-A(4) 1886.77 353.56 481.61 71.02 2523.03 1444.22 6760.21

12 QA-C(5) 1831.02 2028.17 348.40 213.01 920.51 3094.92 8436.03

12 QA-B(6) 869.99 29186.73 3383.39 2660.43 37432.32 29865.49 103398.35



D - 18



13 QC-A(1) 1390.54 0.00 120.76 534.76 0.00 810.00 2856.05

13 QC-B(2) 1060.38 520.64 216.90 1375.56 1156.62 5037.49 9367.59

13 QB-C(3) 1087.83 645.81 145.52 1305.65 297.98 1446.58 4929.38

13 QB-A(4) 1579.50 164.93 297.07 53.54 2121.14 972.58 5188.76

13 QA-C(5) 1532.44 1650.79 182.35 173.38 741.04 2437.21 6717.22

13 QA-B(6) 791.67 21576.11 2414.20 2059.88 27590.88 22077.88 76510.62




13.2 QC-A(1) 1335.96 0.00 90.53 513.77 0.00 752.86 2693.11

13.2 QC-B(2) 1004.74 505.66 185.39 1357.27 1141.24 5022.72 9217.01

13.2 QB-C(3) 1026.16 644.72 109.10 1302.71 223.39 1388.36 4694.44

13.2 QB-A(4) 1518.04 127.21 260.16 50.05 2040.76 878.25 4874.47

13.2 QA-C(5) 1472.73 1575.32 149.14 165.45 705.14 2305.67 6373.45

13.2 QA-B(6) 776.01 20053.98 2220.37 1939.77 25622.59 20520.35 71133.08




14 QC-A(1) 1117.62 0.00 0.00 429.80 0.00 524.29 2071.70

14 QC-B(2) 782.16 445.72 59.37 1284.11 1079.72 4963.64 8614.71

14 QB-C(3) 800.52 602.73 0.00 1237.34 0.00 1118.41 3759.00

14 QB-A(4) 1272.23 0.00 112.89 36.07 1723.54 524.29 3669.01

14 QA-C(5) 1233.87 1273.41 16.30 133.74 561.57 1779.51 4998.40

14 QA-B(6) 713.35 13965.49 1445.02 1459.33 17749.44 14290.27 49622.89




15 QC-A(1) 844.70 0.00 0.00 324.84 0.00 238.57 1408.11

15 QC-B(2) 503.93 370.80 0.00 1192.66 1002.83 4889.78 7959.99

15 QB-C(3) 596.87 410.10 0.00 955.97 0.00 642.82 2605.76

15 QB-A(4) 964.95 0.00 0.00 18.59 1355.82 238.57 2577.93

15 QA-C(5) 936.43 868.48 0.00 91.21 382.09 1094.65 3372.87

15 QA-B(6) 635.03 6354.87 475.83 858.78 7908.00 6502.65 22735.17



D - 19

Intersection – 10



VC-A' QC-A(1) 1092.97 0.00 753.22 3531.57 0.00 1472.94 6850.70

VC-B' QC-B(2) 880.11 559.45 753.22 0.00 0.00 3791.32 5984.10

VB-C' QB-C(3) 880.11 677.00 833.47 0.00 421.56 1146.39 3958.53

VB-A' QB-A(4) 881.53 0.00 753.22 23.59 0.00 1472.94 3131.28

VA-C' QA-C(5) 880.11 1451.30 819.38 0.00 347.52 772.90 4271.21

VA-B' QA-B(6) 880.11 1871.79 753.22 0.00 0.00 570.08 4075.19




10 QC-A(1) 1120.84 1405.45 631.31 3621.60 687.56 3052.52 10519.28

10 QC-B(2) 1632.10 1392.81 645.16 2319.58 854.23 5897.20 12741.09

10 QB-C(3) 1678.18 1694.15 599.35 2202.24 303.15 3337.09 9814.15

10 QB-A(4) 2494.65 0.00 837.53 122.97 3168.49 1667.14 8290.78

10 QA-C(5) 2440.72 4394.35 630.65 260.29 679.59 5998.72 14404.32

10 QA-B(6) 2077.12 0.00 574.16 1186.28 0.00 0.00 3837.55




11 QC-A(1) 982.38 1285.96 475.32 3174.22 629.11 2649.01 9195.99

11 QC-B(2) 1369.42 1252.82 490.02 2188.55 805.98 5759.20 11865.99

11 QB-C(3) 1454.68 1486.70 441.59 1971.41 223.35 2846.89 8424.63

11 QB-A(4) 2188.47 0.00 656.19 102.69 2805.05 1381.43 7133.83

11 QA-C(5) 2140.58 3792.63 471.59 224.65 584.26 5119.87 12333.58

11 QA-B(6) 1809.80 0.00 423.02 1067.04 0.00 0.00 3299.87




11.3 QC-A(1) 940.84 1250.11 428.52 3040.00 611.57 2527.96 8799.00

11.3 QC-B(2) 1290.61 1210.82 443.48 2149.24 791.50 5717.81 11603.46

11.3 QB-C(3) 1387.63 1424.47 394.26 1902.16 199.41 2699.84 8007.77

11.3 QB-A(4) 2096.62 0.00 601.78 96.61 2696.01 1295.71 6786.74

11.3 QA-C(5) 2050.54 3612.11 423.87 213.96 555.67 4856.22 11712.36

11.3 QA-B(6) 1729.60 0.00 377.68 1031.27 0.00 0.00 3138.56



D - 20



12 QC-A(1) 843.92 1166.46 319.32 2726.83 570.65 2245.51 7872.69

12 QC-B(2) 1106.73 1112.82 334.87 2057.52 757.73 5621.21 10990.88

12 QB-C(3) 1231.19 1279.26 283.82 1740.58 143.55 2356.70 7035.10

12 QB-A(4) 1882.30 0.00 474.84 82.41 2441.60 1095.71 5976.87

12 QA-C(5) 1840.44 3190.90 312.53 189.01 488.94 4241.03 10262.84

12 QA-B(6) 1542.48 0.00 271.89 947.81 0.00 0.00 2762.18




13 QC-A(1) 705.46 1046.96 163.33 2279.44 512.19 1842.01 6549.39

13 QC-B(2) 844.05 972.83 179.73 1926.49 709.47 5483.21 10115.78

13 QB-C(3) 1007.69 1071.82 126.06 1509.75 63.76 1866.51 5645.58

13 QB-A(4) 1576.13 0.00 293.50 62.13 2078.15 810.00 4819.91

13 QA-C(5) 1540.30 2589.17 153.47 153.37 393.61 3362.18 8192.10

13 QA-B(6) 1275.17 0.00 120.76 828.57 0.00 0.00 2224.50




14 QC-A(1) 566.99 927.47 7.34 1832.05 453.73 1438.50 5226.09

14 QC-B(2) 581.36 832.83 24.58 1795.47 661.22 5345.22 9240.68

14 QB-C(3) 788.93 851.38 0.00 1266.86 0.00 1363.50 4270.68

14 QB-A(4) 1269.95 15.45 112.39 41.86 1717.51 539.52 3696.67

14 QA-C(5) 1240.19 1986.28 0.00 117.65 298.28 2482.19 6124.60

14 QA-B(6) 1007.85 0.00 0.00 709.34 0.00 0.00 1717.19




15 QC-A(1) 428.53 807.97 0.00 1384.67 395.27 1035.00 4051.44

15 QC-B(2) 318.68 692.84 0.00 1664.44 612.96 5207.22 8496.14

15 QB-C(3) 588.98 579.29 0.00 976.06 0.00 809.58 2953.91

15 QB-A(4) 963.78 113.55 0.00 21.58 1371.83 350.50 2821.23

15 QA-C(5) 940.82 1351.49 0.00 80.05 202.95 1570.75 4146.06

15 QA-B(6) 740.54 0.00 0.00 590.10 0.00 0.00 1330.64


Acknowledgement

While writing dissertation on ”Traffic Engineering”, there are many individuals who assistedand encouraged directly or indirectly. I want to take the opportunity to acknowledge theirassistance and encouragement here.

First and foremost I want to thank Prof. Dr.-Ing. Werner Brilon, my doctoral advisor, whoallowed me to study and to do research at Lehrstuhl für Verkehrswesen, Ruhr–UniversitätBochum, who agreed to walk with me as I struggled to create something new. I shall neverforget his willingness to guide me through my work. His words of encouragement, quieturgings and careful reading of all of my writings will never be forgotten. He is one of the rareadvisors that students can ever find.

Secondly, I would like to thank my co-advisor, Dr.-Ing. habil. Ning Wu for sparing hisvaluable time, technical support and pieces of precious advice through out the study. Hismotivating words, fruitful discussions and deliberated kindness with various mathematicalapproaches considerably contributed to the success of this work and I am glad he was there.

Thirdly my words of thanks go to Prof. Dr. Henk van Zuylan from Transport Research CenterDelft (TU Delft) and Prof. Dr.-Ing. Hermann Orth who kindly agreed to be co-referee andexternal examiner. Their critics and useful suggestions were highly acknowledged.

Also, I pay thanks to the Technological and Professional Skills Development Sector Project(TPSDP Project) Batch II – University of Tanjungpura, Pontianak, Deutscher AkademischerAustausch Dienst (DAAD) and Lehrstuhl für Verkehrswesen, Ruhr–Universität Bochum forfinancial support. Without their support, I could not have done what I was able to do.

Further, my humble thanks go to all technicians; Marco Hehn, Jürgen Banken and DirkKriebel for many helps and technical discussions in connection with the preparation of fieldinvestigations, measurement and computer. Also I thank secretary, Mrs. Heike Rohde-Durhack, Mrs. Karin Kockel at the library of the Institute and Deputy Head of the Institute,Dr.-Ing. Reiner Wiebusch-Wothge who provided a good and encouraging environment in theInstitute, very pleasant atmosphere, support in the administrative matters and providingreferences/books during the whole study.

Special thanks for other members of the Institute; Dr.-Ing. Thorsten Miltner who helped mefrom the very moment and guided me during the period of the research preparation, Dr.-Ing.Justin Geistefeldt, Dr.-Ing. Jochen Harding, Dipl.-Ing. Christina Betz, Dipl.-Ing. Anja Estel,Gui Fang Yang M.Sc., Dipl.-Ing Ralph König, Dipl.-Ing. Axel Geppert, Dipl.-Ing. ThomasWietholt, Mrs. Petra Martin, and Mrs. Bianca Schacht for sharing knowledge and experience,a very pleasant and sympathetic working atmosphere since my first day in Germany.

The acknowledgement would remain incomplete without a heart felt thanks to my dear friendsand colleagues who supported me throughout all of the technological and academicchallenges. They helped me find resources at campus, offered help to trouble-shoot mylanguage in dissertation, and stood with me at my defenses to make sure that the piece ofresearch study worked practically. I am really unable to find due words to say thanks to them,however, I will not be able to forget their kindness ever.

I would also appreciated all the time the friendship of Noreddine Belguesmia M.Sc., a friendfrom Mostaganem, Aljazair.

I also thanks to my friends in Indonesia, who supported and helped me during the fieldinvestigation in Pontianak, West–Kalimantan.

Finally, I want to express my thankful feelings for my family; my wife Erna Setia Putrishowed unwavering faith in me all the time, She supported me down to the loving tears whichshe shed at my defense. Our daughter Aroe Ajoeni Sulistyorieni and our son Bimo KuncoroYakti Prasetijo are also there to be thanked for their innocent support and encouragementalong the way – teasing me as I struggled with the technology as well as the ideas.

My mother RA. Lilik Astuty, my father Sedijono Kusumohamidjojo SH., my mother-in-lawHjh. Zaleha and all of my brothers and sisters bore witness and affirmed as a representative ofmy family the completion of a long and thought provoking journey. Their prayers helped meto make the transition from where I came to where I am. They believed that I could doanything even when I could not find very strong reason to believe in myself.

I dedicate this dissertation to them. They gave me the courage to take risk of being mycreative self wherever that may lead me.

Bochum, August 2007

Curriculum Vitae

Name : PrasetijoFirst Name : JoewonoDate of Birth : 18. October 1969Place of Birth: Pontianak, IndonesiaAddress : Laerheidestraße 10, App. 6 E03

44799 Bochum

Education : 1976 – 1981 Basic School SD Bruder Nusa Indah, Pontianak

1981 – 1982 Basic School SD Kayu Putih 12 Pagi, Jakarta

1982 – 1985 Elementary School SMP St. Fransiskus II, Jakarta

1985 – 1988 Senior High School SMA Negeri 31, Jakarta

08/1988 – 01/1993 Bachelor of Engineering Faculty of Civil Engineering Tanjungpura University, Pontianak

10/1994 – 09/1995 Diploma Post Graduate in Transportation and Road Engineering Department of Civil Engineering, IHE – Technical University Delft, The Netherlands

10/1995 – 09/1996 Master of Science in Road Engineering Department of Civil Engineering, IHE – Technical University Delft, The Netherlands

03/1998 – 09/1998 Individual Training Course in Research Study in Civil Engineering, Department of Civil and Environmental Engineering, Nagaoka University of Technology, Japan

since 02/2004 Doctoral student at Lehrstuhl für Verkehrswesen, Fakultät für Bauingenieurwesen, Ruhr–Universität Bochum, Germany

Occupation : 02/1993 – 07/1993 Road Engineer PT Indah Kusuma Jaya Engineering Consultant, Pontianak

08/1993 – 09/1997 Teaching Staff Faculty of Civil Engineering Panca Bhakti University, Pontianak

03/1998 – now Teaching Staff Faculty of Civil Engineering Tanjungpura University, Pontianak

Bochum, August 2007

capacity and performance

Documents

quantityof traffic

mode characteristics

transport mode hierarchy

different characteristics

lehrstuhl fr verkehrswesen

performance of operation

concentration of vehicles

research methodology