wanyiri s k - traffic congestion in nairobi cbd

8/9/2019 Wanyiri S K - Traffic Congestion in Nairobi CBD

1/65

JOMO KENYATTA UNIVERSITY

OF

AGRICULTURE AND TECHNOLOGY

DEPARTMENT OF CIVIL, CONSTRUCTION AND ENVIRONMENTAL

ENGINEERING

ECE 2505

PROJECT REPORT

TITLE


2/65

TITLE

E25-0133/04

This project is submitted in partial fulfillment of the award of BSc. Civil Engineering of the Jomo Kenyatta

University of Agriculture and Technology

April 2010


3/65

DECLARATION

―I Steve K Wanyiri do declare that this report is my original work and to the best of my knowledge, it has

not been submitted for any degree award in any University or Institution.‖

Signed…………………………………… (Author) Date……….…………………………

E25-0133/04

CERTIFICATION

―I have read this report and approve it for examination.‖

Signed……………………………………… (Supervisor) Date………….……………………

MR. M.O. WINJA


4/65

E25-0133/04

ACKNOWLEDGEMENT

Foremost, I most thankful my Lord and Saviour Jesus Christ, for all He is to me.

I appreciate my supervisor, Mr. M. O. Winja for the support and direction that has contributed in making this

project a success.

I am ever grateful to my lecturer and friend Dr. Z. C. Abiero-Gariy, for his solid and unwavering guidance

and advice throughout the project.

I am highly indebted to Mr. Linus Tonui for their technical support in data gathering and analysis.

I thank my parents, Job and Beatrice, my siblings Becky, Frank, Julita and Mary, and my cousin Jane for the

love, emotional and financial support they have given me.

I also appreciate the prayers, support and encouragement of Benjamin K Mwanzia.

Finally, I acknowledge the support of all my friends, relations and classmates.


5/65

E25-0133/04

ABSTRACT

Traffic congestion is an increasing problem in many urban environments and Nairobi is not an exception.

The main objective of this research was to establish the main causes of traffic congestion in Nairobi. The

study aims at giving recommendations to the problems of congestion by considering, as a case study, the

Nairobi central business district (CBD). Specifically, the study applied data from the City Council of Nairobi

to examine how various factors influence the capacity of the road network. This was achieved by evaluating

the traffic volumes and travel speeds of selected corridors within the CBD, and examination of the network

characteristics of the study area. Mathematical analysis by use of the MS Excel spreadsheet was applied in

the evaluation of the data as well as the application of ArcGIS software in network analysis. The data

analysis showed that lack of pedestrian facilities and numerous traffic incidents are solvable problems thatcontribute to traffic congestion in the central business district.


6/65

E25-0133/04

Table of Contents

ACKNOWLEDGEMENT ................................................................................................................................. ii

ABSTRACT ..................................................................................................................................................... iii

List of Tables ................................................................................................................................................... vii

List of Figures.................................................................................................................................................. vii

Chapter 1 ........................................................................................................................................................... 1

1.0 INTRODUCTION ....................................................................................................................................... 1

1.1 PROBLEM BACKGROUND AND STUDY JUSTIFICATION ........................................................... 1

1.2 PROBLEM STATEMENT...................................................................................................................... 2

1.3 RESEARCH OBJECTIVES: ................................................................................................................... 3

1.4 RESEARCH HYPOTHESIS ................................................................................................................... 3

1.5 LIMITATIONS OF THE STUDY .......................................................................................................... 3

Chapter 2 ........................................................................................................................................................... 4

2.0 LITERATURE REVIEW ............................................................................................................................ 4

2.1 INTRODUCTION ................................................................................................................................... 4

2.2 CAUSES OF TRAFFIC CONGESTION ................................................................................................ 4

2.3 FACTORS INFLUENCING TRAFFIC CONGESTION ....................................................................... 7

2.3.1 Capacity ............................................................................................................................................ 7

2.3.2 Intersections ...................................................................................................................................... 7

2.3.3 Transportation network ..................................................................................................................... 9

2.3.4 Volume of traffic .............................................................................................................................. 9


7/65

E25-0133/04

2.6.5 Decongestion measures .................................................................................................................. 16

Chapter 3 ......................................................................................................................................................... 17

3.0 RESEARCH METHODOLOGY .............................................................................................................. 173.1 DATA COLLECTION .......................................................................................................................... 17

3.1.1 Secondary data ................................................................................................................................ 17

3.1.2 Data on travel time ......................................................................................................................... 17

3.1.3 Measurement of Distances .............................................................................................................. 18

3.1.4 Data on Traffic counts .................................................................................................................... 19

3.1.5 Pedestrian Counts ........................................................................................................................... 20

3.1.6 Primary data .................................................................................................................................... 20

3.2 DATA ANALYSIS ............................................................................................................................... 20

3.2.1 Evaluation of the travel speeds ....................................................................................................... 20

3.2.2. Peak Hour Factor (PHF) ................................................................................................................ 21

3.2.3 Composition of traffic .................................................................................................................... 223.2.4 Traffic incidents .............................................................................................................................. 22

3.2.5 Lane widths .................................................................................................................................... 22

3.2.6 Network Analysis ........................................................................................................................... 23

3.2.7 Network coverage ........................................................................................................................... 23

3.2.8 Network structure ........................................................................................................................... 23

3.2.9 Costing Congestion......................................................................................................................... 25

Chapter 4 ......................................................................................................................................................... 26

4 0 ANALYSIS RESULTS AND DISCUSSION 26


8/65

E25-0133/04

4.2.4Traffic Incidents .............................................................................................................................. 36

4.2.5 Parking and Capacity of lane widths .............................................................................................. 37

Chapter 5 ......................................................................................................................................................... 385.0 CONCLUSIONS AND RECOMMENDATIONS .................................................................................... 38

5.1 CONCLUSIONS ................................................................................................................................... 38

5.2 RECOMMENDATIONS ...................................................................................................................... 38

REFERENCES ................................................................................................................................................ 40

Appendix A ..................................................................................................................................................... 42

Travel Time data and Travel Speed Results ................................................................................................ 42

Data on Lane widths .................................................................................................................................... 47

Appendix B:..................................................................................................................................................... 48

Traffic Data ................................................................................................................................................. 48

Pedestrian Counts ........................................................................................................................................ 52

Parking and Congestion Data ...................................................................................................................... 52Appendix C: Photographs ................................................................................................................................ 53


9/65

E25-0133/04

List of Tables

Table 1: Level of service comparisons ............................................................................................................ 13

Table 2: Passanger car unit (pcu) conversion factors ...................................................................................... 21

Table 3: Recommended urban carriageway widths ......................................................................................... 23

Table 4: Classification of Networks ................................................................................................................ 24

Table 5: Computed running and journey speeds ............................................................................................. 29

Table 6: Parking Supply and Demand in the CBD .......................................................................................... 33

List of FiguresFigure 1: Map of the Nairobi CBD, Source: Google Maps ............................................................................. 25

Figure 2: Section speeds for Trip 1 ................................................................................................................. 26




Figure 6: Section speeds for Trip 5 ................................................................................................................. 28Figure 7: Total inbound and outbound traffic count for Haile Selassie Avenue ............................................. 29

Figure 8: Inbound and Outbound traffic count for Uhuru Highway................................................................ 30

Figure 9: Compositions of traffic for Haile Selassie Avenue and Uhuru Highway ........................................ 30

Figure 10: Peak hour variation for Haile Selassie Avenue .............................................................................. 31

Figure 11: Peak hour variation for Uhuru Highway ........................................................................................ 31

Figure 12: Pedestrian count on Uhuru Highway/ City-Hall way junction ...................................................... 32

Figure 13: Pedestrian count on Uhuru Highway/ University way junction ..................................................... 32


10/65

E25-0133/04

Chapter 1

1.0 INTRODUCTIONTraffic congestion is a condition on road networks that occurs as use increases, and is characterized by

slower speeds, longer trip times, and increased queuing. The most common example is the physical use of

roads by vehicles. When traffic demand is great enough that the interaction between vehicles slows the speed

of the traffic stream, congestion is incurred. As demand approaches the capacity of a road (or the

intersections along the road), extreme traffic congestion sets in, and when vehicles are fully stopped for

periods of time, this is colloquially known as a traffic jam (Wikipedia, 2009).

Traffic demands vary significantly depending on the season of the year, the day of the week, and even the

time of the day. Moreover, the definition of congestion also varies significantly from time to time and place

to place based on user expectations.

Congestion can be measured in a number of ways – level of service (LOS), speed, travel time, and delay are

the commonly used measures.

This problem robs part of the value of highway investment by causing the highway‘s capacity to be

diminished below the capacity it is capable of conveying. Congestion, in other words, creates massive social

and investment inefficiency by actually diminishing the performance capacity of an existing infrastructure

asset.

Time is literally money. A direct linkage exists between transportation investment, travel conditions

(congestion) and economic productivity. Transportation accounts for a share of the final price of a product,

ranging from one percent to 14 percent, depending on the commodity and the distance moved (U.SDepartment of Transportation, 2005). All this adds up to a staggering amount of costs imposed on travelers

by congestion.


11/65

E25-0133/04

The problem of traffic congestion in many cities of the developing world is blatantly apparent. Traffic

congestion is a thorn in the flesh not only to the country‘s economy but also its well being. Road hazards,

fuel consumption, local air pollution, green-house gas emissions and waste of time/money could only be

some of the problems that are measurerable. In fact the environment and human health costs of roadtransport are part of a growing global concern that is reaching overwhelming proportions. Urban air

pollution causes over 800,000 deaths each year with more than 70% in developing countries. An estimated

70-90% of air pollution in urban areas is as a result of road transport (UNEP, 2009). Traffic congestion is at

the core of this problem.

In most major cities in Africa more than 50% of all trips are non-motorized, mainly on foot. In Nairobi, 60%

of road users walk or cycle, 35% use public transport and only 5% use private cars (UNEP, 2009), yet

decongestion measures suggested are not usually inclusive of this fact. It is therefore appropriate in dealing

with this issue to address the major cause of congestion rather than just a section of it.

According to a recent evaluation in a local daily (Sunday Nation, 21 st Aug. 2009), the man-hours lost in just

about an hour of the usual traffic jam in the larger Nairobi runs into billions of shillings. When mobility of

traffic is stalled, our resources are wasted. But while much is being done especially concerning Vision 2030,

urgently needed is sanity on our roads, which cannot wait until year 2030!

1.2 PROBLEM STATEMENT

Traffic congestion in Nairobi‘s Central Business District is worsening at an alarming rapidity. The problems

associated with the traffic congestion are costing the country substantially in terms of its annual Gross

Domestic Product, yet the more the implementation of proposed solutions is delayed or dragged, the more

the country loses.

As a non-productive activity for most people, congestion reduces regional economic health. Wasted fuel

increases air pollution and carbon dioxide emissions (which contributes to global warming) owing to

increased idling, acceleration and braking, increased fuel use may also in theory cause a rise in fuel costs.

Besides, it contributes to stressed and frustrated motorists, encouraging road rage and reduced health of


12/65

E25-0133/04

1.3 RESEARCH OBJECTIVES:

General objective

To determine the major causes of traffic congestion in Nairobi‘s Central Business District with a view togiving suitable recommendations for alleviating the problem

Specific objectives

i) To determine the major causes of traffic congestion in the central business district (CBD)

ii) To measure the level of traffic congestion in the CBD

iii) To estimate the total economic loss as a result of traffic congestion

iv)

To recommend economically viable ways of mitigating the problem

1.4 RESEARCH HYPOTHESIS

The problem of traffic congestion is underestimated in terms of efforts channeled towards alleviating it.

1.5 LIMITATIONS OF THE STUDYThe study was limited by the following factors:

i) Length of the study: time given for the study was short, and therefore, it was not be possible to

collect all data required, especially primary data.

ii) Study resources: the monetary amount allocated for the study was not adequate; and the availability

of research personnel to assist in the study was also a challenge. Moreover, some data requested

from the City Council of Nairobi was lacking because it was last through an inferno, such as the

capacity of various corridors.

iii) Li i f h d h d i li i d h l b i di i (CBD) f N i bi


13/65

E25-0133/04

Chapter 2

2.0 LITERATURE REVIEW2.1 INTRODUCTION

According to the Nairobi Metropolitan Traffic Decongestion Program (2009), the core City of Nairobi is

experiencing the highest level of immigration resulting into very high pressure on the carrying capacity of

physical and social infrastructure. The most prominent manifestation of this scenario is the persistence traffic

congestion being experienced in the Central Business District (CBD). Previously this was a peak hour issue

but currently traffic snarl up is noticeable anytime of the day and in all the directions. Ultimately Nairobi

Metropolitan residents are making location decisions not based on any economic but traffic situation. For the

last 20 years traffic management measures have been discussed but with little implementation. As a result

the region and the country as a whole are losing approximately Ksh. 30 Billion daily on lost fuels, stress,

time and Environmental degradation (Nairobi Metropolitan Region Decongestion program, 2009).

A report by a group of expert researchers in traffic operations from Organizations for Economic Cooperation

and Development (OECD) and European Conference of Transport Ministers (ECMT) countries (2004) noted

that Road traffic congestion poses a challenge for all large and growing urban areas. They indicate thatCongestion is one of the major pre-occupation of urban decision-makers. A quick scan of policy statements

from across OECD/ECMT cities highlights the importance of congestion to the public, elected officials and

road and transport administrations in many urban areas. Yet, there is little consensus across the

OECD/ECMT member countries on the types of policies that are best suited to tackling congestion in cities.

There is perhaps even less consensus on what precisely congestion is, whether or not it is a ―solvable‖

problem and, in some locations and cases, whether it is problem at all.

In Nairobi, however, congestion is relatively easy to recognize — roads filled with cars, trucks, and buses,

sidewalks filled with pedestrians. In the transportation realm, congestion usually relates to an excess of

vehicles on a portion of roadway at a particular time resulting in speeds that are slower — sometimes much


14/65

E25-0133/04

effectiveness of incident response strategies, roadwork scheduling and prevailing atmospheric conditions

(OECD, 2004).

The FHWA (2009) identifies seven major root causes of traffic congestion grouped into three broadcategories which often interact with each other. They compose both recurrent and non-recurrent congestion:

i)

Traffic-Influencing Events

Traffic Incidents – Are events that disrupt the normal flow of traffic, usually by physical impedance

in the travel lanes. Events such as vehicular crashes, breakdowns, and debris in travel lanes are the

most common form of incidents. In addition to blocking travel lanes physically, events that occur on

the shoulder or roadside can also influence traffic flow by distracting drivers, leading to changes indriver behavior and ultimately degrading the quality of traffic flow. Even incidents off of the

roadway (a fire in a building next to a highway) can be considered traffic incidents if they affect

travel in the travel lanes.

Work Zones – Are construction activities on the roadway that result in physical changes to the

highway environment. These changes may include a reduction in the number or width of travel

lanes, lane "shifts," lane diversions, reduction, or elimination of shoulders, and even temporary

roadway closures. Delays caused by work zones have been cited by travelers as one of the most

frustrating conditions they encounter on trips. In Nairobi, many work zones exist due to road-works

presently on-going.

Weather – Environmental conditions can lead to changes in driver behavior that affect traffic flow.

Due to reduced visibility, drivers will usually lower their speeds and increase their headways when

precipitation, bright sunlight on the horizon, fog, or smoke are present. Wet roadway surface

conditions will also lead to the same effect even after precipitation has ended. Inclement weather – poor visibility and slippery road surfaces cause drivers to slow down.

ii) Traffic Demand


15/65

E25-0133/04

highway section. Capacity is determined by a number of factors: the number and width of lanes and

shoulders; merge areas at interchanges; and roadway alignment (grades and curves). There is also a

wild card in the mix of what determines capacity — driver behavior. Research has shown that drivers

familiar with routinely congested roadways space themselves closer together than drivers on lesscongested roadways. This leads to an increase in the amount of traffic that can be handled. Traffic

signals, freeway ramp meters, and tollbooths are all examples of this type of bottleneck.

Congestion results from one — or the interaction of several — of these causes on the highway system. The

interaction can be complex and varies greatly from day-to-day and highway-to-highway. The problem is that

most of these causes of congestion occur with maddening irregularity — nothing is ever the same from one

day to the next! One day commuters might face low traffic volumes, no traffic incidents, and good weather;

the next day traffic might be heavier than normal, it might be raining, and a severe crash may occur that

blocks traffic lanes. This makes it harder in identifying a single cause for traffic congestion.

As vehicles are forced to get closer and closer together, abrupt speed changes can cause shock waves to form

in the traffic stream, rippling backward and causing even more vehicles to slow down. Disorderly vehicle

maneuvers caused by events have a similar effect on traffic flow as restricted physical capacity (Wikipedia,

2009).

There are a number of specific circumstances which cause or aggravate congestion; most of them reduce the

capacity of a road at a given point or over a certain length, or increase the number of vehicles required for a

given volume of people or goods. About half of U.S. traffic congestion is recurring, and is attributed to sheer

weight of traffic; most of the rest is attributed to traffic incidents, road works and weather events (Wikipedia,

2009). Speed and flow can also affect network capacity though the relationship is complex.

As if the congestion picture was not complicated enough, FHWA (2009) indicate that some events can cause

others to occur. For example:

The presence of severe congestion can reduce demand by shifting traffic to other highways or cause

travelers to leave later High congestion levels can also lead to an increase in traffic incidents due to


16/65

E25-0133/04

base capacity are less vulnerable to disruptions: a traffic incident that blocks a single lane has a greater

impact on a highway with two travel lanes than a highway with three travel lanes. This reinforces the notion

that adding physical capacity is a viable option for improving congestion, especially when made in

conjunction with other strategies.

However, inherent risks of building too much roadway capacity include increased urban sprawl, higher air

pollution levels, heavy reliance on the auto-mobile and negative impacts on the communities that border

major transportation corridors.

2.3 FACTORS INFLUENCING TRAFFIC CONGESTION

2.3.1 CapacityThe capacity of a highway may be described as its ability to accommodate traffic. It has been defined as the

flow which produces a minimum acceptable journey speed and also as the maximum traffic volume for

comfortable free-flow conditions. The Highway Capacity Manual defines capacity as the maximum hourly

rate at which persons or vehicles can reasonably be expected to traverse a point or uniform section of a lane

or roadway during a given time period under prevailing roadway, traffic and control conditions.

Salter (1989) points out that highway capacity is limited by:

1. The physical feature of the highway, which do not change unless the geometric design of the

highway changes.

2.

The traffic conditions, which are determined by the composition of the traffic.

3. The ambient conditions which include visibility, road surface conditions, temperature and wind

The demand for road space, especially in existing central town areas, will always be greater than the supply

because even if the necessary financial resources were available there would be conflicting demands for the

available land. The fact that available demand for road space is greater than the supply results in traffic


17/65

E25-0133/04

Roundabouts

The capacity of a conventional roundabout is directly affected by the capacity of each weaving section

incorporated within the intersection. If any of the weaving sections is overloaded, then locking of theroundabout may occur and it can be said that the capacity of the roundabout is exceeded.

Within a particular weaving section, true non-stop weaving will only occur when the headways between the

vehicles are of sufficient lengths and frequencies that safe merging and diverging movements can take place.

Discontinuous flow, due to stop-go movements of the weaving vehicles, occurs when these headways are not

available, or when the weaving section length is so short that the paths of the weaving vehicles cross at large

intersecting angles.

The main factors controlling the capacity of a conventional weaving section are the geometric layout,

including entrances and exits, and the percentages and composition of the weaving traffic.

Signalized intersections

Several studies have shown that signalized cross intersections are more favourable over roundabouts. For

signalized intersections, what is meaningful is not the capacity of the intersection but the capacity of an

approach or a lane or a lane group of an intersection (Chakroborty and Das, 2003).

One of the disadvantages of traffic circles is that they cannot be controlled by signals as effectively as

ordinary intersections because of the complexity of the vehicle paths. By using traffic signals to control

vehicles entering the traffic circle, the intersection‘s capacity is much less than if the streets crossed directly.

A standard signal controlled intersection allows opposing directions of traffic to flow simultaneously so that

each approach could be served for nearly half the cycle time (Daganzo et al, 2009).

In a signal-controlled cross intersection (as opposed to a traffic circle), only the approach upstream of the

intersection is blocked, and traffic headed in the crossing direction is not impeded. The traffic approaching

from the direction of the queue spillback will always be able to discharge into any of the possible


18/65

E25-0133/04

One way streets

One way street systems are those in which motor-vehicle movement on any carriageway within the system is

limited to one direction; they are generally considered to be one of the simplest tools for relieving trafficcongestion without expensive reconstruction or excessive policing. Their most effective usage is in the

congested central areas of cities where the possibilities of utilizing more extensive aids to traffic movement

are often very limited.

2.3.3 Transportation network

The purpose of an urban street network is to provide accessibility for people in a city. To maximize

accessibility for a given distribution of the traveling population, the street network should serve as many

trips as possible.

Urban traffic is a chaotic system in which small disturbances can result in very different traffic conditions on

individual streets. Although traffic conditions can vary greatly from street to street, the collective

performance of all the streets in a neighborhood such as a city center is more consistent (Daganzo et al,

2009).

The description of transportation network can be undertaken at different levels of detail and requires

specification of its structure, its properties or attributes and the relationship between those properties and the

traffic flows. The analysis of a network is carried out to achieve several objectives which include:

minimization of traffic congestion (which is the main focus), minimization of travel distance or time,

maximization of accessibility and maximization of network densities. A mature transportation network is

key to dealing with the problem of traffic congestion .

2.3.4 Volume of traffic

Usually, the volume of traffic on a given road section fluctuates widely with time. The rate of flow willdepend on the speed, traffic density and the headway. The distribution of headways will depend on the

traffic volume and also on the capacity of the highway. Salter (1989) says that if the drivers cannot maintain

their desired speed by overtaking slower moving vehicles then free flow conditions no longer exist and the


19/65

E25-0133/04

i) Pedestrian channelization

By pedestrian channelization is meant the use of footpaths in conjunction with guardrails or barriers so that

pedestrians are kept off the carriageway at certain locations. With the exception of the regulations affectingthe special roads, there is no specific law which says that pedestrians must use the footpath and not the

carriageway. Thus in congested locations, or where the pathway is cracked and uneven, or when the

pedestrian simply wishes to cross the road, he is at liberty to step on the carriageway at any time and at any

place.

ii) Traffic signals

Traffic signals are used in a variety of ways to control pedestrian movement across the carriageway. By farthe most widely used procedure is simply to allow the pedestrians to cross ―with the lights‖ when the

opposing vehicular traffic is normally brought to a standstill at a junction. Although this is quite efficient in

the great majority of cases, problems may arise through conflicts between pedestrian flow and the turning

vehicles. When this occurs a separate pedestrian phase may have to be included in the signal cycle. When

pedestrian volumes are very high, vehicular traffic is moderate, and the streets are so narrow that it is not

possible to have separate traffic lanes for turning and straight-ahead traffic, consideration should be given to

the use of an all-red ‗scramble‘ period during which the pedestrians can take the shortest way across theintersection rather than the traditional regular route.

There can be however, considerable disadvantages to providing separate pedestrian phases at important

intersections. The most critical of these is concerned with the signal times required to accommodate both the

pedestrians and the moving vehicles. On the one hand, the pedestrian phase must be sufficiently long to

ensure that it is completely safe for the pedestrian to cross; on the other hand, the consequent reduction of

time available for the other traffic movements often necessitates a substantial lengthening of the signal cycle

so that the vehicular traffic can be accommodated. The net result is that the signal cycle may be so long that pedestrians do not wait for the period allotted to them and cross during traffic phases. If the cycle length is

reduced to satisfy the pedestrian requirements, then the free movement of vehicles may be impaired.


20/65

E25-0133/04

locations such as exits from schools, recreation grounds, footpaths or passages, along busy shopping streets,

and adjacent to zebra crossing, signals and segregated crossings.

However, there are inherent trade-offs between different forms of accessibility. This occurs because roadwaydesign and land use patterns optimal for one mode are generally less suited for other modes. As a result, land

use patterns that maximize automobile access (low density development with activities located along

arterials and highway intersections) tend to have poor transit and non-motorized access, while transit-

oriented development (clustered development with limited parking and good pedestrian access) may increase

traffic and parking congestion. Wide roads and higher traffic speeds tend to create barriers to walking, so

vehicle and pedestrian street design objectives often conflict. (Todd Litman, 2008).

2.3.6 Pubic transport

While traffic management and urban highway construction have their place in minimizing congestion it is

now generally accepted that, without the dispersal of town centre activities, the only solution at the present

time is a greater emphasis on public transport.

If this transfer from individual to public transport is accepted as part of a solution to the problems of traffic

in towns, then it will be necessary to find some means of traffic restraint. At the present time congestion

itself acts as a restraint, causing trips which would take place at congested periods to be made at other times,or by alternative non-congested modes, or the trips may not be made at all. Congestion is however an

inefficient mode of restraint in that the priority of service is first come, first served, regardless of the value of

the trip to either the trip-maker or the community. It is inefficient in the use of resources and is detrimental

to the environment adjacent to the facility (Salter, 1989).

There are generally three ways in which restraint could be applied. Firstly the entry of vehicles to certain

areas at certain times could be prohibited by administrative means. Secondly, restraint could be applied by

use of parking regulations, especially the restriction of long-term parking, which is characteristic of carcommuting. Thirdly is road pricing whereby users of congested roads would be charged according to the

distance travelled or the time spent on them, at varying rates governed by the degree of congestion (Salter,

1989)


21/65

E25-0133/04

during incident-free periods. Non-recurrent congestion is usually assumed to be equal to the recurrent

congestion. Other congestion-related performance measures include travel rate, percent facility segments

with demand higher than capacity, or threshold speeds. In general, however, there is a lack of consistent

definition and measurement of the congestion and its components using real-world data.

Roadway congestion delay consists of recurrent delay plus the additional (non-recurrent) delay caused by

accidents, breakdowns, and other random events, such as inclement weather and debris. Recurrent delay

arises from fluctuations in demand, the manner in which the freeway is operated, as well as the physical

layout of the roadway. Non-recurrent delay depends on the nature of the incident: an accident is likely to

cause more delay than a vehicle stopped on the shoulder of the highway (Karl F. Petty, et al, 2003).

Therefore, since delay is a random quantity, it is also acceptable that a single sample measurement of thedelay — as is commonly done by measuring the delay experienced by a single probe vehicle run — does not

provide a meaningful estimate of this delay.

One of the principles that the U. S. Department of Transportation has established for monitoring congestion

as part of its annual performance plan is that meaningful congestion performance measures must be based on

the measurement of travel time. Travel times are easily understood by practitioners and the public, and are

applicable to both the user and facility perspectives of performance (FHWA, 2009).

2.4.1 Temporal Aspects of Congestion:

Measuring congestion by times of the day and day of week has a long history in transportation. A relatively

new twist on this is the definition of a weekday "peak period" — multiple hours rather than the traditional

peak hour. In many metropolitan areas, particularly the larger ones, congestion now lasts three or more hours

each weekday morning and evening. In other words, over time, congestion has spread into more hours of the

day as commuters leave earlier or later to avoid the traditional rush hour (FHWA, 2009). Definition of peak

periods is critical in performing comparisons. For example, consider a three-hour peak period. In smallercities, congestion may usually only last for one hour — better conditions in the remaining two hours will

"dilute" the metrics. One way around this is not to establish a fixed time period in which to measure

ti b t th d t i h l ti i t ( t f ti h ti


22/65

E25-0133/04

Table 1: Level of service comparisons

Level of

service

A

B

C

D

E

F

Percent of road

capacity used

50 – 59%

60 – 69%

70 – 79%

80 – 89%

90 – 99%

100%

Freeway

speeds

more than 60 mph

57 – 60 mph

54 – 57 mph

46 – 54 mph

30 – 46 mph

less than 30 mph

Street

speeds

more than 35 mph

28 – 35 mph

22 – 28 mph

17 – 22 mph

13 – 17 mph

less than 13 mph

Source: Highway Capacity Manual (2000)

This measure is based on an A through F grading scale. Under this scale, traffic conditions are best at LOS

A. Moving down the scale, traffic conditions incrementally deteriorate to the worst condition – LOS F.

When speeds are used in measuring congestion, they factor both the temporal and special aspects.

2.5 COSTING CONGESTION

The cost to the trip-maker has been referred to as the private cost of a journey while the cost that the trip-

maker imposes upon other trip-makers because of an increase in congestion is referred to as the congestion

cost. The addition to the total costs caused by one extra trip-maker is the marginal cost. It consists of the

private costs of the additional trip together with the congestion costs caused by the additional trip. Inaddition to these costs are the environmental costs that the trip imposes upon the area adjacent to the

highway, and the road maintenance costs. Private costs include fuel, maintenance, depreciation, and those

associated with the value of time (Salter 1989)


23/65

E25-0133/04

Pollution costs — greenhouse gas emissions, other pollutants such as nitrous oxide, sulphur dioxide,

particulate matter, noise and others.

Reduced amenity — long queues of traffic can impact upon people and districts in many ways.Some people may find that it is harder to walk through an area, or it is less pleasing to do so.

It can be agreed that the economy of this country hinges heavily on small businesses, in one way or the

other. For small businesses in industries like agriculture, manufacturing and retail, roads and highways are

critical arteries of commerce. However, traffic on those roads and highways is taking a bite out of profit.

There is evidence that business views traffic congestion as causing a serious problem and believe that it

causes a significant cost imposition. A survey from the United Kingdom found that traffic congestion was perceived as the most important factor likely to affect costs and service in the next three years (Fernie et al,

2000). Managers of trucking companies operating in California in the United States expressed a similar

sentiment with 80 per cent of managers indicating that traffic congestion was a ‗somewhat serious‘ or

‗critically serious‘ problem (Centre for International Economics, 2006).

Congestion may actually produce potential benefits for business. For example, businesses along a popular

shopping strip might benefit from an increase in passing trade due to congestion. Identifying whether

particular businesses, or even whole industries, benefit from congestion is an important consideration when

measuring the net costs of congestion to businesses.

Therefore the resulting traffic slowdowns can have a wide range of negative impacts on people and on the

business economy, including impacts on air quality (due to additional vehicle emissions), quality of life (due

to personal time delays), and business activity (due to the additional costs and reduced service areas for

workforce, supplier and customer markets). There is no single rule of thumb for the economic cost of

worsening congestion or the economic benefit of congestion reduction, for that can also differ depending onthe area‘s specific economic profile, as well as its unique pattern of congestion.

While it is clear that increasing traffic congestion does impose costs upon travelers and affect broader


24/65

E25-0133/04

2.6.1 Street network

Nairobi‘s streets are hierarchical. Major arterials are paved and serve the purpose of connecting

neighborhoods while local streets are often inadequately maintained and offer poor connectivity. In turn,

traffic is concentrated onto the main streets and the side streets cannot feasibly serve through traffic. Trafficin Nairobi is concentrated on the larger roads connecting the various neighborhoods in the city. Due to land

use patterns that favor suburban housing, there is strong peaked flow drawing people into the city center

each morning and out to the surrounding neighborhoods each evening.

According to Gonzales, (2009), the small number of streets in Nairobi results in the following conditions:

• Concentration of Vehicles on Limited Infrastructure – Since there are few streets, and most arterials

are radial, vehicular trips between different neighborhoods must share limited paved street space,concentrating traffic onto the sparse network of major roads. This is particularly problematic in and

around the CBD.

• Lack of Redundancy – The connections between the major arterials are few and far-between, so there

are usually no more than one or two reasonable routes for any origin-destination pair. This means

that traffic cannot be redistributed to use street infrastructure more efficiently. Due to the lack of

ring roads many peripheral trips must pass through the CBD which compounds traffic congestion in

the center.

2.6.2 Public transport

The wealthiest residents of Nairobi tend to travel by private vehicles or taxi, and they are motivated in large

part by concerns about the safety and security of traveling by matatu or walking. On the other end of the

socio-economic spectrum is the vast population who cannot afford to travel by any means other than

walking. The largest slums lie within a few miles of the city center, so residents travel on foot. Where matatu

service becomes critically important is in connecting the city center to outlying townships and communities.Where distances are too far to walk, matatus provide the only affordable means of transport for many people.

Of the nearly 4 8 million trips made each day in Nairobi in 2004 only 16% were made in private vehicles;


25/65

E25-0133/04

queue spillbacks when the network becomes congested. This undesirable effect occurs because the traffic

circle serves all directions simultaneously on the same circular section of road. Traffic circles tend to spread

congestion faster than if intersections were signal controlled. This problem is especially debilitating at the

few traffic circles through which all traffic entering and exiting the CBD must flow (Daganzo et al, 2009)

Turning maneuvers that interrupt regular traffic flows are particularly problematic in Nairobi because there

are many un-signalized intersections, where left turning vehicles can cause substantial traffic delays.

When queues of traffic entering the city in the morning back up to traffic circles upstream, vehicles exiting

the city center are blocked. In the evening, the jamming of a traffic circle further reduces the rate at which

trips can depart the city center. Consequently, vehicles accumulate more quickly and contribute to

widespread gridlock in the city center. Therefore, it is important that critical intersections are designed to prevent this kind of locking, and that road use policies do not cause queues of traffic to spill back into traffic

circles (Daganzo et al, 2009).

2.6.4 Conflicting modes

The urban transport system in Nairobi is characterized by a poorly connected street network crowded with

competing modes of transport (Gonzales et al, 2009). As the mode share of motorized transport increases,

there is a need to rationalize the way the network is shared by private and public vehicles(Gonzales et al,

2009).

Pedestrians are also considered to contribute greatly to traffic congestion through interactions that cause

inefficiencies for vehicular traffic operations. These interactions also represent a severe safety hazard for

people walking in the streets.

2.6.5 Decongestion measures

The proposed Nairobi Metropolitan Region Decongestion program (2009) measures will include amongothers the following interventions;

1 one way (uni-direction) traffic movement along Moi Avenue Koinange Street Tom Mboya Street


26/65

E25-0133/04

Chapter 3

3.0 RESEARCH METHODOLOGYThe research methodology is split into two phases. The first phase is the collection of data and the other the

analysis and application of the data.

3.1 DATA COLLECTION

Two types of data were used in the study namely: secondary and primary data, whose descriptions are as

follows:

3.1.1 Secondary data

The project is based majorly on data provided by the City Council of Nairobi. The data is supplemented with

information in the report entitled ―The Study on Master Plan for Urban Transport in the Nairobi

Metropolitan Area in the Republic of Kenya‖ (Katahira & Engineers International, 2006). It includes

Recent traffic volume counts of Haile Selassie Avenue and Uhuru Highway,

Pedestrian counts for junctions of Uhuru Highway with City Hall way and University way,

Travel time data,

Parking data, and

Road congestion data

The data provided was raw, as had been collected directly from the field in hand written (hard copy) form.

Therefore, this required feeding the data into a computer (softcopy form) before any analysis using Excel

spreadsheet application was made. Supplementary data from other studies was used for comparisons and to

check on consistencies and disparities.

A map of the study area (in digital form) provided by Geomatics Engineering and Geospatial Information

Systems (GEGIS) department of Jomo Kenyatta University of Agriculture and Technology (JKUAT) was


27/65

E25-0133/04

Trip 2

Trip 2 was from Uhuru Highway/Haile Selassie roundabout, past Haile Selassie Avenue, Moi

Avenue, and Kenyatta Avenue up to State-House road. It was carried out on 28

th

August, 2009 between 10:20 hours to 10:46 hours.

Trip 3

Trip 3 was the return journey for trip 2, from State-House road back to Uhuru Highway/Haile

Selassie roundabout, and was carried out the same day, at 10:48 hours to 11:12 hours.

Trip 4

The probe vehicle traversed from Uhuru Highway/Kenyatta Avenue roundabout, past Kenyatta

Avenue, Moi Avenue, and through Haile Selassie Avenue up to Retail Market. It was carried out

between 15:38 to 15:55 hours on 1st September, 2009.

Trip 5

Trip 5 was the return journey for trip 4, from Retail Market through the same way back to UhuruHighway/Kenyatta Avenue. It was carried out the same day, at 15:57 hours to 16:18 hours.

The data on travel times was collected using a probe vehicle as it moved through the aforementioned

corridors as the travel times were recorded. The points of data recording were as follows:

On Haile Selassie Avenue: Retail Market, Toilet No.8, Haile Selassie/Moi Avenue roundabout, L.T.

Tumbo, Parliament road and Haile Selassie/Uhuru Highway roundabout.

On Kenyatta Avenue: State-House road, Uhuru Highway/Kenyatta Avenue roundabout, Koinange

street, Muindi Mbingu street, Wabera street, Kimathi street, and Kenyatta Avenue/Moi Avenue

j ti


28/65

E25-0133/04

3.1.4 Data on Traffic counts

Traffic volume study was conducted to determine the number and classifications of roadway vehicles within

the CBD. These data was used to identify critical flow time periods, determine the influence of large

vehicles and pedestrians on vehicular traffic flow, and show traffic volume trends.

Manual counts of 15-minute intervals were used to obtain the traffic volume data for Haile Selassie Avenue

and Uhuru Highway road sections. The corridors were chosen majorly due to availability of data. The

manual counts were conducted simply by means of recording data onto tally sheets. The data was recorded

with a tick mark on a pre-prepared field form. A watch was used to measure the count intervals. Traffic flow

was recorded at 15 minute intervals because it is the longest period that traffic flow can be considered to

remain constant.

The categorized vehicle counts were carried out at census points which were determined by the City Council

as Railway Club and the Railway Bridge for Haile Selassie Avenue and Uhuru Highway sections

respectively.

The data was recorded separately for different legs and for period. The legs included those ―outbound‖ for

traffic headed away from the central business district, and those ―inbound‖ for traffic headed towards the

CBD.

The data studied for traffic volume and pedestrian counts was limited to only the two corridors which were

used as samples since the study area was large.

Vehicle categorization

The method of vehicle categorization as used by the City Counsel of Nairobi is:

1. Cars/Vans

Small cars- capable of carrying up to five persons including the driver

Large cars- 4WD vehicles such as Land Rovers, Toyota Land Cruisers, Mitsubishi Pajeros, etc,


29/65

E25-0133/04

3.1.5 Pedestrian Counts

Pedestrian count data are used frequently in planning applications. Pedestrian counts in this study were used

to evaluate the influence of pedestrians to traffic flows.

Pedestrian counts were conducted the same way as the traffic volume counts for Uhuru Highway /City Hall

way junction and Uhuru Highway/University way junction.

Data on the pedestrian count is summarized in Appendix B.

3.1.6 Primary data

This is data collected first hand from the study area considered. This included field observations, of which

some were captured by use of a camera. This data showed traffic incidents such as poor driving,inappropriate parking, conflicts between pedestrians and motorists, conflicts at un-signalized intersections

and off-peak traffic congestion.

The data was used to prove the reality of traffic incidents which reduce the capacity of the roadway and

thereby contributing to non-recurrent traffic congestion.

The photographs are shown in Appendix C.

3.2 DATA ANALYSIS

To determine the causes and level of congestion, the following were done:

i) evaluation of the travel speeds

ii) evaluation of the peak hour factor

iii) assessment the composition of traffic

iv) examination of the network characteristics and properties of the study area

v) assessment the capacity of the CBD corridors (lane widths)

3.2.1 Evaluation of the travel speeds


30/65

E25-0133/04

Graphs were obtained to show the variation of the section speeds by use of the same spreadsheet application.

The variation of the section speeds was used to indicate the traffic flow conditions, whether stable or

unstable. This was also used to show driver freedom on the corridors.

For each single trip, the journey speed and the running speeds were obtained for the for the probe vehicle.

The mean of the section speeds was obtained (using the spreadsheet) as the running speed for a given trip.

This measure of the average speed was more appropriate for the description of the stream conditions as it

gives a measure of the traffic stream over space (Chakroborty and Das, 2003). Using Table 1 the level of

service (LOS) was also determined based on the speeds.

The journey speed was calculated as the cumulative total time divided by the cumulative total distance. Themeasure of journey speeds included stops that were as a result of congestion (non-recurrent on the roadway

and recurrent at intersections).

3.2.2. Peak Hour Factor (PHF)

Capacity and other traffic analyses focus on the peak hour of traffic volume, because it represents the most

critical period for operations and has the highest capacity requirements (HCM, 1994). Peak rates of flow are

related to hourly volumes through the use of the peak-hour factor (PHF). This factor is defined as the ratio of

total hourly volume to the peak rate of flow within the hour.

The traffic count data provided was as obtained in the field in units of vehicles/hour (veh/h). The traffic

volume however, should be in terms of passenger car units per hour (pcu/h).The heavy-vehicle adjustment

factors applied in this study were those used by the City Council of Nairobi, for a level terrain in an urban

area as shown in Table 2.

Table 2: Passenger car unit (pcu) conversion factors

Traffic category Factor


31/65

E25-0133/04

The peak hour factor (PHF) is a very important parameter as it is descriptive of trip generation patterns as

applies to a street section. Peak-hour factors in urban areas generally range between 0.80 and 0.98. Lower

values signify greater variability of flow within the subject hour, and higher values signify little flow

variation. Peak-hour factors over 0.95 are often indicative of high traffic volumes, sometimes with capacityconstraints on flow during the peak hour.

Using of equation 1, the PHF was computed by the MS Excel spreadsheet. Graphs were drawn to show the

variation of PHF during the day. Analysis of PHF was used in this study to indicate the volumes of traffic

and the concentration of traffic flows on the roadway.

3.2.3 Composition of traffic

Traffic composition has a vital effect on capacity and other design considerations. The greatest difference between different types of vehicles is reflected in the overtaking times required by the heavier vehicles.

Roads with heavy vehicles (trucks, buses, etc) have less capacity than those without; hence it was important

to determine the composition of various categories of traffic in order to determine its influence on the traffic

congestion within the CBD.

From the classified traffic counts, the composition of different categories of traffic was computed by use of

the MS Excel spreadsheet. The compositions were represented in a pie chart to give a clear comprehension

of the information.

3.2.4 Traffic incidents

It has been found that individual incidents (such as accidents or even a single car braking heavily in a

previously smooth flow) may cause ripple effects which then spread out and create a sustained traffic jam

when, otherwise, normal flow might have continued for some time longer. In a high density of traffic, small

disturbances such as a driver hitting the brake too hard or getting too close to another car can quickly

become amplified into a full-blown, self-sustaining traffic jam (Wikipedia, 2009). This study sought to findout the influence of traffic incidents on congestion within the CBD.

Although there lacks any documentation of traffic incidences in Nairobi number of photographs were taken


32/65

E25-0133/04

The number of traffic lanes to be used in a specific situation is dependent on the volume and type of traffic

that has to be handled. The adequacies of the lanes in the study area were checked against Table 3 on the

recommended carriageway widths.

Table 3: Recommended urban carriageway widths

Road type Description of carriageway Carriageway width, m

Primary distributor Dual 4-lane

Overall width for 4-lane carriageway with

central refugees

14.60

14.60

District distributor Single 2-lane (normal)

Dual 2-lane (normal)

7.30

7.30

Local distributor Single 2-lane, in industrial districts

Single 2-lane, in principle business districts

7.30

6.75

Source: O’Flaherty, 1974

3.2.6 Network Analysis

The purpose of an urban street network is to provide accessibility for people in a city. To maximize

accessibility for a given distribution of the traveling population, the street network should serve as many

trips as possible. The network of the study area was analyzed to asses its maturity and adequacy.

Physical characteristics of a road network in an area can be categorized into qualitative and quantitative

characteristics. Qualitative characteristics are shown on maps. The map provided in Figure 1 shows the

fi ti tt t f li k d d b d h t i ti f th t ffi id


33/65

E25-0133/04

classification of the network configuration from maturity to immaturity is given by spinal (immature), grid

(transitional) to delta (maturity) using values shown in Table 4.

i)

The Beta or Gamma index

This index measures connectivity of the network in terms of the degree to which all pairs of nodes are

interconnected from a minimal to a maximum level. It is defined as the ratio of actual links to the maximum

expected links in the network as follows:

(2)

WhereG = the gamma/beta index

е = the actual number of links

v = number of nodes in the network

ii)

The Alpha index

This index measures circuitry of the network, that is, the degree to which the pairs of the modes have

alternative links between them. It is defined as the ratio of actual number of circuits to the maximum number

of circuits as follows:

(3)

Where e and v are as defined above, and A is the alpha index

In order to classify a given network by type of the above three configurations, limiting values for gamma andalpha indices have been established in Table 4 as follows:

Table 4: Classification of Networks


34/65

E25-0133/04

3.2.9 Costing Congestion

The economic cost of traffic congestion was calculated as shown in section 4.1.7 the computationsconsidered only the direct costs (section 2.5) of fuel and average income of motorists. The estimated

economic cost was determined to show not only what is approximately lost as a result of congestion of

traffic, but also the important fact that the financial resources channeled towards alleviation of traffic

congestion cannot compare to what is forfeited through neglect.


35/65

E25-0133/04

Chapter 4

4.0 ANALYSIS, RESULTS AND DISCUSSION4.1 ANALYSIS AND RESULTS

Analysis was done using the MS Excel software. Most of the calculations are therefore not shown in this

section, but the formulae were applied as explained in section 3.2 of the Research Methodology (Chapter 3).

However, the calculations in Network analysis and estimation of the cost of congestion are explained.

The following results were obtained from the analysis of Data:

4.1.1 Travel Speeds

Figures 2 through 6 show the results of the computed travel speeds for the five trips. Table 5 shows the

computed running and journey speeds. Appendix A shows the complete data and results for all the five trips

in tabular form.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

k e t

o . 8

R . A b

o a d

u r u …

t t a …

e e t

e e t

e e t

e e t

a v e …

e e t

a y

a v e

a v e …

o . 8

k e t

S p e e d ( k m / h )

Trip 1

section speed


36/65

E25-0133/04

Figure 3: Section speeds for Trip 2

0.00

10.00

20.00

30.00

40.00

50.00

60.00

S p e e d ( k m / h )

Stations

Trip 2

Section speed

35.00

40.00

45.0050.00

/ h )

Trip 3


37/65

E25-0133/04

Figure 5: Section speeds for Trip 4

0.005.00

10.0015.0020.0025.0030.00

35.00

S p e e d ( k m / h )

Stations

Trip 4

speed

10.00

15.00

20.00

25.00

e e d ( k m / h )

Trip 5


38/65

E25-0133/04

Table 5: Computed running and journey speeds

Trip 1 Trip 2 Return Trip 2 Trip 3 Return Trip 3

Running speed 15.85 17.75 15.68 12.27 11.09

Journey speed 10.12 6.45 6.97 7.55 6.38

4.1.2 Traffic Count

From 11 hour the traffic counts done on the two selected corridors - Haile Selassie and Uhuru highway – thefollowing figures were developed: Figures 7 and 8 represent the variation of traffic flow; the pie charts in

figure 9 were used to capture the composition of traffic while Figures 10 and 11 show the variation of peak

hour factor during the day.

Pedestrian counts for Uhuru Highway/City-Hall way junction and Uhuru Highway/University way

roundabout are shown in Figures 12 and 13.

The graphs and charts were developed from the traffic data represented in Appendix B, which shows the restof the data and results.

3000

35004000

4500

u r

Total inbound and outbound traffic count

for Haile Selassie Avenue


39/65

E25-0133/04

Figure 8: Inbound and Outbound traffic count for Uhuru Highway

Composition of Traffic

0

1000

2000

3000

4000

5000

6000

7 : 0 0 - 8 : 0 0

8 : 0 0 - 9 : 0 0

9 : 0 0 - 1 0 : 0 0

1 0 : 0 0 - 1 1 : 0 0

1 1 : 0 0 - 1 2 : 0 0

1 2 : 0 0 - 1 3 : 0 0

1 3 : 0 0 - 1 4 : 0 0

1 4 : 0 0 - 1 5 : 0 0

1 5 : 0 0 - 1 6 : 0 0

1 6 : 0 0 - 1 7 : 0 0

1 7 : 0 0 - 1 8 : 0 0

p c u / d a y

Time

Inbound and Outbound traffic count for

Uhuru Highway

Outbound

Inbound

2% 5%

Uhuru Highway1% 2%

Haile Selassie Avenue


40/65

E25-0133/04

Peak Hour Factor

Figure 10: Peak hour variation for Haile Selassie Avenue

0.00

0.20

0.40

0.60

0.80

1.00

1.20

7 : 0 0 - 8 : 0 0

8 : 0 0 - 9 : 0 0

9 : 0 0 - 1 0 : 0 0

1 0 : 0 0 - 1 1 : 0 0

1 1 : 0 0 - 1 2 : 0 0

1 2 : 0 0 - 1 3 : 0 0

1 3 : 0 0 - 1 4 : 0 0

1 4 : 0 0 - 1 5 : 0 0

1 5 : 0 0 - 1 6 : 0 0

1 6 : 0 0 - 1 7 : 0 0

1 7 : 0 0 - 1 8 : 0 0

p h f

Time

PHF

1 00

1.20

PHF


41/65

E25-0133/04

Pedestrians

Figure 12: Pedestrian count on Uhuru Highway/ City-Hall way junction

0

200

400

600

800

1000

1200

1400

1600

7 : 0 0 - 8 : 0 0

8 : 0 0 - 9 : 0 0

9 : 0 0 - 1 0 : 0 0

1

0 : 0 0 - 1 1 : 0 0

1

1 : 0 0 - 1 2 : 0 0

1

2 : 0 0 - 1 3 : 0 0

1

3 : 0 0 - 1 4 : 0 0

1

4 : 0 0 - 1 5 : 0 0

1

5 : 0 0 - 1 6 : 0 0

1

6 : 0 0 - 1 7 : 0 0

1

7 : 0 0 - 1 8 : 0 0

h o u r l y c o u n t s

Time

Pedestrian count on Uhuru Highway/

City-Hall way junction

houirly variation of

number of pedestrian

1400

Pedestrian count on Uhuru Highway/

University way junction


42/65

E25-0133/04

4.1.3 Network analysis

The number of links, e was counted as 147.

The number of nodes or vertices; v was counted as 92.

The equations 2 and 3 were applied as shown:

The values of gamma/beta (G) and alpha (A) indices were computed as 0.54 and 0.31 respectively. From

Table 4, the Nairobi‘s CBD was judged to have a Grid (transitional) network class .

4.1.5 Lane widths

The carriageway widths for most of the major corridors such as Uhuru Highway, Haile Selassie Avenue,

Moi Avenue, Kenyatta Avenue and University way were more than 30m, some being 50m with a service

road. The data obtained shows that most of the access lanes range between 3.6m - 6.5m, with two extremecases of 8.5m as the widest and 2.5m as the least.

4.1.6 Parking

Table 6: Parking Supply and Demand in the CBD

Total On-Street Off-Street Building

Capacity 14,864 3941 3834 7089

= D/C 140% 95% 50%


43/65

E25-0133/04

On average, 50% of the vehicles on a road section are cars, and about 40% are matatus (approximating from

the composition of traffic data in section 4.1.2). The average length of a matatu or car is about 4m. Allowing

for a gap of 1m, this gives about 5m of distance headway. If a traffic jam covers a kilometer of a road

section, then it bears about 200 vehicles. If the matatu carries 14 people, then in total, there are100x14=1400passangers and if a car carries 1 person, then there are 0.4x200x1=80 people. Thus, a kilometer

of traffic jam carries about 1480 people. Therefore, an hour wasted in a kilometer of traffic jam will cost

about 120x1480=177,600/=.

An idle vehicle consumes about a litre per hour. If a litre costs Kshs.100 (for calculation purposes) then 200

vehicles in a kilometer of traffic jam in an hour will cost 200x100=20,000/=.

Therefore in total, an hour of traffic jam covering a kilometer of road section costs20000+177600=197,600/=, say Kshs. 200,000.

This calculation covers only income costs and fuel costs. The study area in the central business district

(CBD) has about 20km of road, and these traffic jams may extend for more than an hour. Hence the exact

direct cost as a result of congestion may run into millions of shillings under different conditions.

4.2 DISCUSSION

4.2.1 Travel speeds

It was observed from Figure 2 that the speeds were highest on Haile Selassie Avenue‘s section between L.T.

Tumbo and Parliament Road (63km/h), and on Moi Avenue‘s section between Harambee Avenue and Moi

Avenue/Haile Selassie roundabout (40km/h). From Figures 3 and 4 the speeds on the same section between

L.T. Tumbo and Parliament Road on Haile Selassie Avenue were again very high (54km/h and 46km/h).

These consistently high speeds on the road section regardless of the direction are attributed to the presence

of a pedestrian footbridge on this section which effectively separates pedestrians from motorists.

Moreover, the trend in Figures 3 and 4 also show that when the speeds in the outbound traffic flow are high,

those in the inbound flow are low and vice verse (with the exception of the road section from LT. Tumbo to


44/65

E25-0133/04

It was deduced that there were unstable flow conditions on trips 2, 3 4 and 5 because of the zigzag nature of

the graphs, and sometimes the flow was forced (very low speeds of below 5km/h) because of the high

congestion levels. Such an irregular kind of traffic flow may be a consequence of both fixed and operational

delays because of many intersections and the interacting effects of traffic on the highway or street, and is anindication of limited freedom for the driver.

From all the five trips, the average running speed was found to be 14.53km/h (computed from Table 5). The

running speed is the average speed maintained by a vehicle over a given route while the vehicle is in motion.

It is used for the purpose of determining road capacity and level of service (LOS) of a given road. From the

results in the Figures 2 through 6, the low speeds depicted are also indicative of forced flow conditions, and

minimum driver freedom. Thus, it can be conjured, using Table 1, that the LOS in Nairobi‘s CBD is LOS F

(which can generally be said of most other corridors). This is because the running speed is much less than 21km/h (13 miles/hour). This finding is also supported by the discussion in section 4.2.2

Journey speed is the total distance travelled divided by the total time taken to cover the distance. The total

time includes both the running time and the time when the vehicle was not moving (at intersection and traffic

jams). It is used to measure traffic congestion as a general adequacy or inadequacy of a road. From the

values shown in Table 5, the congestion level is very high. The average journey speed is 7.49km/h while the

recommended speed in urban areas in Kenya is usually 50km/h. However, the greatest problem in the traffic

flow is not so much as the slow speed as in the congested or stop-and-go traffic flow.

4.2.2 Traffic Volumes

From Figure 7, it is notable that the evening traffic volume is higher compared to the morning traffic

volumes since the graph shoots up increasingly towards the evening peak period. This information suggests

that there is greater traffic congestion in the evening when queues from vehicles trying to leave the city

center spill back into the central business district (CBD). This is consistent with the data from Katahira &

Engineers International (2006), where congested conditions in the city center are more extensive during theevening peak than in the morning. Traffic volumes are maximized during the evening because there are

many internally generated trips which will tend to jam the road network thus increasing the congestion.


45/65

E25-0133/04

Pedestrians

Figures 12 and 13 show the number of pedestrians is congruent to the variation of the vehicular traffic

volume, that is, high in the morning, over lunch hour and evening. Therefore, there are great interactions

between motorists and pedestrians throughout the day.

From section 4.2.1, the section between L.T. Tumbo and Parliament road has relatively very high speeds

than the rest of the road sections because of the provision of a footbridge which clearly eliminates the

conflict between motorists and pedestrians. Hence, from this study, it can be conjured that the conflict

between pedestrians and motorists due to lack of adequate pedestrian infrastructure greatly contributes to

traffic congestion.

The relatively high rate of pedestrian conflicts with motorists is in part due to the lack of alternative

infrastructure for people to walk. The absence of sidewalks (those present are narrow and inadequate) forces

pedestrians to walk along the shoulders and traffic lanes of busy roads as shown on the photographs

(appendix C). The crowded state of infrastructure is further exacerbated by the encroachment of markets and

commercial activities onto transport right of way. This puts very diverse modes (from pedestrians to

motorists) on an even narrower road space.

Compositi on of Traff ic

From the pie chart representation in Figures 9, it is inferable that Cars/Vans and Matatus form the largest

composition of traffic within the central business district (CBD), and therefore, contribute most to the traffic

congestion. The heavy goods vehicles, light goods traffic and buses are not a big hindrance to the traffic flow

because of their low volume (about 10% to 13%). Therefore, in dealing with traffic congestion in the CBD,

the cars and matatus should be the priority rather than heavy vehicles or buses.

4.2.3 Network characteristics

From table 4, Nairobi‘s CBD was judged to have a Grid (transitional) network class. That is, while the

network is not mature enough with adequate links and nodes neither is it immature However Gonzales et


46/65

E25-0133/04

―wave effect‖ of delay. Traffic congestion also varies with weather conditions. In the study, ―Multimodal

Transport modeling for Nairobi,‖ Gonzales et al, (2009), argues that rainy weather reduces the free-flow

speed of vehicles by approximately 10%, lane discharge capacity by 40%, as well as adding a couple of

seconds delay to start-up loss time for vehicles at intersections, thus reducing the capacity of Nairobi‘sintersections greatly.

Although there lacks any recording or documentation of such events, traffic incidents are as rampant in the

city as shown in the photographs (Appendix C).

4.2.5 Parking and Capacity of lane widths

With respect to parking space, the data provided (Table 6) shows that utilization of the parking space has not

been maximized. Photographs of the area (appendix C) show that there are poor parking practices within theCBD, with flush type of parking being used by large vans. Hence, it is inferable that poor parking practices

may contribute to reduced capacity of the roadway, hence traffic congestion. Moreover, parking facilities

contribute to increased influx of motorists to the central business district (CBD), thereby increasing

congestion.

The capacity of signalized intersections as such is not meaningful. What is meaningful is the capacity of an

approach or a lane or a lane group of an intersection Thus, lane widths were determined to judge whether the

capacity or road-space in the CBD meet the provisions in the Highway Capacity Manual as regards urban

roads. From the observations, it is clear that the capacities of traffic lanes are sufficient for urban roads in

comparison to Table 1. However, this does not mean that the capacity provided meets the required demand,

but it is a pointer that the severe traffic congestion experienced in the CBD is not a consequence of

inadequate capacity per se, but also poor traffic management.


47/65

E25-0133/04

Chapter 5

5.0 CONCLUSIONS AND RECOMMENDATIONS5.1 CONCLUSIONS

The following conclusions were drawn from the study:

1. One of the most important factors restricting the capacity of Nairobi‘s central business district

(CBD), according to the findings in this study, is the presence of pedestrians who use the streets for

transport and commerce. Pedestrian barriers alone are not enough in separation of pedestrians frominterrupting motorists, and despite their presence, there are still conflicts of pedestrians and

motorists. Only one footbridge exists within the CBD.

2. The second important finding in this study is the contribution of traffic incidents (non-recurrent

congestion) to the recurrent traffic congestion. While there is the problem of the inadequacy of the

base capacity, the problem of congestion is exacerbated through poor driving practices, poor parking

practices, and inefficient traffic management within the central business district (CBD).

3. Thirdly, travel on Nairobi‘s street network is of transitional maturity (it is not yet matur e). There are

few streets, few unreliable intersections, along with the limited available routes. Therefore,

congestion can arise unexpectedly and last for hours because there are missing links to divert traffic

around incidents or locations of congestion.

4. There is very high traffic congestion in the Nairobi central business district (CBD) as a result of the

interaction of both recurrent and non-recurrent congestion. Judging by the low traffic speeds, the

high traffic volumes and little freedom for drivers, the level of service in Nairobi ‘s CBD is LOS F.

5. The approximated average cost on income and fuel use as a result of traffic congestion was found to

be Kshs. 200, 000 per hour per km of road section

5.2 RECOMMENDATIONS


48/65

E25-0133/04

Therefore, it is imperative to direct research resources on ways to possibly curb unruly driving and

ensure disciplined drivers in order to ensure future development is not futile. Moreover, while there

is no intention to downplay the importance of the proposed decongestion measures which basically

aimed at increasing the base capacity, it should be noted that the proposed improvements will dolittle if traffic management is not streamlined.

In addition, of importance in the recommendations is traffic efficiency education and regulation for

public transport related persons, such as drivers, conductors and users as one of the most important

measures to prepare convenient transport system in Nairobi central business district.

3. A feature of signalized intersections which does not appear to be used currently as a traffic control

strategy is variation of signal times for different times of the day. This could be used to restrict entry

to the center during times of day when the CBD is likely to be congested and to increase the capacity

of routes exiting the city center in order to control vehicle accumulations. With some monitoring

wanyiri s k - traffic congestion in nairobi cbd

Documents