TITLE OF PROJECT

By

“candidates name"

A research proposal submitted in partial fulfilment of the requirement for the award of

“Bachelors Degree in Information Technology”.

2011

DECLARATION

Part (i)

This is my own work and has not been presented previously as a proposal for a degree in

another university. I will carry out the research under the university supervisor whose

name is listed in Part (ii) below:

“Candidates name”

Sign…………………………….Date………………………………….

Part (ii)

Supervisors

This proposal has been submitted by our approval as university supervisors.

1. Jomo .N. Njenga

Mathematics and Informatics Department

J.K.U.A.T – TAITA TAVETA CAMPUS

Sign…………………………….Date………………………………….

i

TABLE OF CONTENTS

DECLARATION..……………………………………………………………….

TABLE OF CONTENTS..………………………………………………………

LIST OF FIGURES..……………………………………………………………

LIST OF ABBREVIATIONS..………………………………………………….

ABSTRACT..…………………………………………………………………….

CHAPTER ONE....................................................................................................1

1.0 INTRODUCTION.............................................................................................11.1 STATEMENT OF THE PROBLEM...............................................................11.2 JUSTIFICATION OF THE STUDY................................................................11.3 OBJECTIVES OF THE STUDY......................................................................11.4 RESEARCH QUESTION.................................................................................1

CHAPTER TWO...................................................................................................2

2.0 LITERATURE REVIEW.................................................................................22.1 GLOBAL PERSPECTIVE...............................................................................22.2 AFRICA PERSPECTIVE.................................................................................3

CHAPTER THREE...............................................................................................3

3.0 REQUIREMENTS ANALYSIS AND SPECIFICATION.............................33.1 POPULATIONS AND SAMPLES...................................................................33.2 SAMPLING PROCEDURE..............................................................................33.3 SOFTWARE REQUIREMENTS DOCUMENT............................................3

3.3.1 Introduction................................................................................................33.3.2 PURPOSE OF THE REQUIREMENTS DOCUMENT........................33.3.3 SCOPE OF THE PROJECT....................................................................3

3.4 GENERAL DESCRIPTION.............................................................................33.4.1 ASSUMPTIONS AND DEPENDENCIES...............................................33.4.2 SPECIFIC REQUIREMENTS.................................................................3

CHAPTER FOUR..................................................................................................3

4.0 CONCEPTUAL FRAMEWORK FOR SOFTWARE ALGORITHM.........34.1 IMPLEMENTATION STRATEGIES.............................................................34.2 OPERATIONAL FUNCTION OF PROPOSED SOFTWARE:...................3

3

CHAPTER FIVE...................................................................................................3

5.0 BUGDET ESTIMATION..................................................................................35.1 RESOURCE REQUIREMENT........................................................................3

5.1.1 PERSONNEL REQUIRED.....................Error! Bookmark not defined.5.2 PERSONNEL COST AND MAN YEARS.....Error! Bookmark not defined.5.3 TRAVELLING, EQUIPMENT, MATERIALS

AND OTHER EXPENSES...............................................................................3

CHAPTER SIX......................................................................................................3

6.0 TIME FRAME...................................................................................................3

REFERENCES………………………………………………………………….21

BIBLIOGRAPHY………………………………………………………………22

4

iiiiii

ii

LIST OF FIGURES

Figure 1. Functions of the algorithm of the proposed I.M.S.T.C system……………1

Figure 2. Operation function environment of the proposed I.M.S.T.C

system……………………………………………………………………5

iv

LIST OF ABBREVIATIONS AND ACRONYMS

v 6

Abstract.

vi 7

CHAPTER ONE

1.0 INTRODUCTION

1.1 STATEMENT OF THE PROBLEM

1.2 JUSTIFICATION OF THE STUDY

1.3 OBJECTIVES OF THE STUDY

The objectives of the study are, to:-

1.4 RESEARCH QUESTION

.

1

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 GLOBAL PERSPECTIVE.

N/B: USE THE HARVARD REFERENCE SYSTEM

The knowledge of the current traffic state is essential for reliable traffic

information and advanced adaptive network control methods. The information

obtained is essential for successful traffic management. Online monitoring of

traffic in metropolitan road networks is only consistent over space and time

to a limited or certain extent. As a way to counter such a problem techniques

such as data fusion have been proposed as a strategic management solution of

metropolitan areas coherently influencing different adaptive control methods.

(Friedrich n.d).

Real time traffic management in urban road networks requires information on the

present and future traffic states. The information required should be complete

and precise as much as possible (Lehnhoff 2004). More information comes from a

variety of sources such as inductive loops, video observation and floating car

data (F.C.D), it has been observed that information obtained from inductive loops

are to a high degree unreliable and do not provide information on the actual

traffic state in terms of level of service, or in terms of queue lengths or delays.

Floating car data was observed that travel time estimates are characterised

by particularly high variances and needs to be processed later (Torday 2004).

Traffic monitoring entails information sourcing from different sources, which can

be divided in measurements such as data collected by detectors or coming from

traffic light timing, etc. and additional information obtained by using the

measurements in estimation algorithms and similar methods. Those additional

information can be the estimated flows within intersections (Cremer 1981),

and (Matschke 2001) and (Mirchandani 2002). The estimated movements at

intersections with traffic lights (Matschke 2001) Propagated link flow counts

(Kimber 1981).

2

A new concept for online traffic state estimation is to split the system into;

1. Network level.

2. Intersection level.

In the intersection level data fusion techniques are applied in real time to

combine detected flow data and information on turning movements, queue

lengths, delay and flow.

On the network level the enhanced data then serves as a basis for the

determination of consistent flows and travel times. (Friedrich n.d). Based on

fusion of traffic counts and traffic light timings data which is available to a

certain degree for all networks, the volumes for all movements within an

intersection are all calculated. The volumes calculated this way are then

propagated to adjacent sectors where accuracy is considered. Thus such

information obtained by this procedure offers the opportunity to obtain more

accurate data at each detected intersection and also fill the data gaps on links

where detectors are not available.

Queue lengths are estimated by using data fusion technique by combining traffic

counts and traffic light timing. Floating car data (FCD) may be used to compare

and calibrate estimation (Friedrich n.d). After link flow information has been

obtained and using a guess of the route choice a first object data (OD) estimation.

A traffic assignment then uses the processed data on vehicle volumes, queue

lengths and OD relations and results in consistent flows and travel times. Based

on the above a new iteration of OD estimation and assignment is carried out.

Floating car data again can be used to compare and calibrate travel times as well

as additional information like weight for OD estimation. (Friedrich n.d).

2.2 AFRICA PERSPECTIVE

2.2.1 SOUTH AFRICA

In South Africa, the improved economic wellbeing has resulted in increased

vehicle purchases despite high fuel prices, thus this scenario has re-emphasised

the need for safe and efficient road systems (Vanderschuren 2006).

The traffic system in South Africa has been structured akin to the American one

where private car ownership is the preference to public transport.

3

(Vanderschuren 2006).

The level of service of public transport is poor, uncomfortable and neither

convenient nor safe. The high private car dependency has resulted in

unsustainable situations of congestion, emissions and noise pollution.

(Vanderschuren 2006)

One of the strategies the South African government is implementing is the

corridor approach where selected axes (Corridors) get better public transport.

The recapitalization of the mini-bus taxi industry by the South African

government is aimed at replacing unroadworthy vehicles on the roads of South

Africa and especially in urban areas (Vanderschuren 2006).

Road management is envisioned by deployment of ITS (Information Traffic

System) technology, which includes a centralized network management centre in

Midrand (NMC), closed circuit television cameras (CCTV), variable message

signs (VMS), loops and other traffic detection and information devices as well as

continuous monitoring of the systems and their impact on improved road network

operations. Further experimentation and research will take place during the course

of the five year operational phase of the pilot project to determine tailor made

solutions for local conditions and road users (SANRAL 2005).

A key component of the project is the interaction and enhancement of existing

incident management system (IMS) in order to facilitate faster emergency and

incident response. This will be achieved by improving lines of communication,

speed and efficiency of notification between the incident location and the IMS

(SANRAL 2005).

It is expected that private cars will play an even greater role in the future

(Vanderschuren 2006).

2.2.2 KENYA

In Kenya, there is a Nairobi Metropolitan Region Development (NMRD) plan that

is coordinated by the Nairobi Metropolitan Ministry, instituted to fast track and

transforms the city of Nairobi to a modern, competitive metropolis and

one of the ` leading destinations in the East and Central Africa region.

4

NMR (Nairobi Metropolitan Region) contributes approximately 60% of the

national DP (Domestic Product) and is home to over 60% of the urban population

and it also receives the highest ratio of foreign and local investments

(NairobiMetro 2030 2008).

The core of NMR which is defined by the county council of Nairobi is

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

the carrying capacity of physical and social infrastructure. A prominent

manifestation of the problem is the persistent traffic congestion being

experienced in the CBD (Central Business District) that leads to the country and

region losing an estimated Kshs. 30 billion daily on lost fuel, stress, time and

environmental degradation. The CBD is actually stalling, with these conditions

(NairobiMetro 2030 2008).

Medium and long term strategies include:-

One way (uni-directional) movement – the capacities in the carriageways

in the CBD are overstretched and cannot allow modern designs to

accommodate multi-directional movement. Thus the solution is to convert

some of the streets for one way traffic (NairobiMetro 2030 2008).

Dedicated bus routes – the Ministry of NMRD aims at re-introducing

buses to the CBD, in consultation with PSV operators, the ministry has

defined the new transit bus routes across the city and not terminating in the

CBD. Within the CBD, there will be an establishment of dedicated lanes

for this service (NairobiMetro 2030 2008).

Removal of on-street parking – removal of the parking spaces will pave

way for the dedicated bus routes (NairobiMetro 2030 2008).

Car park silos – current public car parks are under utilised, the ministry

intends to partner with the private sector to develop modern state of the art

car park silos (NairobiMetro 2030 2008).

Park and ride – this strategy entails private cars are parked at facilities

located conveniently along key roads and the owners approach the CBD

with dedicated transport services (NairobiMetro 2030 2008).

5

Designated drop and pickup points will be established especially of cargo

related transport and other diverse needs (NairobiMetro 2030 2008).

Development of bypass and missing links is already ongoing after

government reclaimed land for this intended purpose (NairobiMetro 2030

2008).

Restriction of heavy traffic transit in the CBD to ease congestion

(NairobiMetro 2030 2008).The CBD is being expanded so as to cope with

demand and thus the reason for the formation of the Nairobi metropolitan

region to improve the quality of life for its citizens (NairobiMetro 2030

2008).

Use of enforcement agencies - currently the traffic police a division of

Kenya police force enforce the highway code and are instrumental in

ensuring that traffic services are rendered and in resolving disputes that

arise when accidents occur (NairobiMetro 2030 2008).

The literature review focussed on the global trends and Africa perspective.

Globally many countries and cities have developed intelligent traffic

systems with an aim of streamlining their systems with ICT technologies

where data fusion, queuing algorithms have been implemented to monitor

the same. In the Africa perspective, e.g. South Africa has put in place an

intelligent traffic system prototype for five years to obtain data and

observations relevant to their environment for implementation. In Kenya,

there is a CBD decongestion strategy that has been developed by the

Nairobi metropolitan region development ministry, there are plans for a

comprehensive traffic policy and implementation for the envision Nairobi

metropolis by the year 2030. The trend identified is the continued

development of intelligent traffic systems especially the data fusion model

provided by (Friedrich n.d), that can be captured to develop an improved

intelligent traffic and communication system.

It is important to note that literature reviewed only covered South Africa

and Kenya that had information on development and improvement plans

and strategies on traffic management.

6

CHAPTER THREE

3.0 REQUIREMENTS ANALYSIS AND SPECIFICATION

3.1 POPULATIONS AND SAMPLES

Target Population

The target population is the population which a researcher wants to

generalize the results of the study (Mugenda .O. and Mugenda .A 1999). The

target population will be roads and highway.

Accessible population

Accessible population will entail road and highway users in the

metropolitan city.

o Sample size

The sample size will be determined from areas with high density

road and highway users.

3.2 SAMPLING PROCEDURE

A sampling frame of 200 respondents will be engaged.

Purposive sampling will be employed to the respondents to obtain relevant data

to the study.

Stratified sampling will be employed to obtain variety of data and information

using resource tools such as questionnaires, interview schedules, online

interactive blogs and observations.

Data obtained from the resource tools will be analysed using SPSS ver. 8.00 and

above.

3.3 SOFTWARE REQUIREMENTS DOCUMENT

3.3.1 Introduction

3.3.2 PURPOSE OF THE REQUIREMENTS DOCUMENT

The requirements document is instrumental and key to show; user requirements of

the proposed system and system requirements of the proposed system.

1. User requirements of proposed system:

7

2. System requirements:

3.3.3 SCOPE OF THE PROJECT

To develop an intelligent management system for traffic and communication

(I.M.S.T.C).

3.4 GENERAL DESCRIPTION

Functional requirements

Functional requirements will be obtained by analysis of questionnaires, interview

schedules and interactive blogs.

Non-functional requirements

Non – functional requirements are the constraints on the services and functions

offered by the system.

The system should, be:

Efficient – should capture, perform comparative analysis and store data

without delay.

Reliable – The system should not fail when handling transactions.

Accessible and transparent – the system should be accessible to authorised

officers and transparent to public systems.

Wired – up – the system should be on a network and all facilities running

smoothly.

Domain requirements

1. The system should be able to capture images, perform comparative

analysis and determine penalties and fines to be paid.

2. Interfaces should be standard to officers but different to other stakeholders

who access the system.

3.4.1 ASSUMPTIONS AND DEPENDENCIES

8

3.4.2 SPECIFIC REQUIREMENTS

9

CHAPTER FOUR

4.0 CONCEPTUAL FRAMEWORK FOR SOFTWARE SOLUTION

Below is a diagrammatical representation of the functions of the proposed

software solution.

10

4.1 IMPLEMENTATION STRATEGIES.

Implementation program;

Implementation Platform:

Equipment and modules to be incorporated to proposed system:.

4.2 OPERATIONAL FUNCTION OF PROPOSED SOFTWARE:

11

CHAPTER FIVE

5.0 BUGDET ESTIMATION

5.1 RESOURCE REQUIREMENT

5.2 TRAVELLING, EQUIPMENT, MATERIALS AND OTHER EXPENSES

ACTIVITY AMOUNT IN Euro (€)

Travel Costs

Traveling expenses (visits to various institutions and

libraries to gather information)

Conferences and workshops (preparation and traveling

expenses)

SUB-TOTAL

1,000

1,000

2,000

Office Equipment

Computer related equipment:

Hardware/ Software

Internet charges

Flash disks, Printing paper:

Textbooks

International journals

SUB-TOTAL

2,000

200

200

1,200

3,000

6,600

Materials, Services and Expendables

Purchase/subscription to both journals and

12

papers

Payment to data collection team

Stationary, photocopying and printing

Data preparation and analysis

Specific communications on the research

E-mail and Internet correspondence

Visits to relevant institutions

Attendance at relevant workshops and

conferences

SUB-TOTAL

300

1,800

600

500

200

200

640

1,200

5,440

Special Activities

Costs of planning and conducting a training seminar

Data collection and team training materials

Allowances for trainers

Refreshments

Reports preparation and manuscripts

publication

SUB-TOTAL

100

300

300

500

1,200

GRAND TOTAL 15,240

Overall cost of the research (€2,000 + € 13,240) = € 15,240/=

N/B: remember to provide a narrative on budget.

13

CHAPTER SIX

6.0 TIME FRAME

N/B: use a Gantt Chart.

14

REFERENCES

Creme, M., Keller, H. (1981). Dynamic Identifications of Flows from Traffic Counts of Complex Intersections. Proc. Of the 8th International Symposium on Transportation and Traffic Theory. (V.F. Hurdle et al. Ed.), University of Toronto Press, Toronto, pp. 121-142.

Friedrich, B. (n.d). Traffic monitoring and control in metropolitan areas. Institute for Traffic Engineering and Planning. University of Hannover. Applestr. 9A 30167 Hannover.

Kimber, R. M., Hollis E. M. (1981): Traffic queues and delays at road junctions. number 909. TRRL Laboratory Report. University of Hannover. Applestr. 9A. 30167 Hannover.

Lehnhoff, N. (2004): Loop Detectors: Accurate and Efficient? Proceedings of the Triennial Symposium on Transportation Analysis TRISTAN V, June 13 - 18, 2004, Le Gosier – French West Indies.

Matschke, I., Friedrich, B. (2001). Dynamic OD Estimation Using Additional Information from Traffic Signal Lights Timing. Proceedings of the Triennial Sym-posium on Transportation Analysis TRISTAN IV, June 13 - 19, 2001, Sao Miguel - Azores, Portugal

Mugenda, M.O. and Mugenda, G.A. 1999. Research Methods-Quantitative and Qualitative approaches. Acts Press. Nairobi, Kenya.

Nairobi Metro 2030 (2008). The Nairobi Metropolitan RegionTraffic Decongestion program. Publication. www.state.go.ke

South African Road Agency Pty Ltd (SANRAL) (2005), Annual Report 2004/2005, Pretoria (SA).

Sommerville, I. (2005).Risk Management: Software Engineering. Seventh edition. Pearson Education. (Ch 5; 5.4).

Torday, A.; Dumont, A.-G. (2004): Probe Vehicles Based Travel Time Estima-tion in Urban Networks. Proceedings of the Triennial Symposium on Transpor-tation Analysis TRISTAN V, June 13 - 18, 2004, Le Gosier – French West Indies.

Vanderschuren, M.J.W.A, Intelligent Transport Systems for South Africa. Impactassessment through microscopic simulation in the South African context, T2006/4, August 2006, TRAIL Thesis Series, The Netherlands.

15

Choi, K. and Jang, W. (2000) Development of a transit network from a street map database with spatial analysis and dynamic segmentation,Transportation Research Part C:Emerging Technologies, Vol. 8, No. 1-6, 129-146.

Clement, S. (1995) The Transport Network Relational Database, University of South Australia, Adelaide, Australia, Working Paper 95/1.

Dia, H. (2001) An object-oriented neural network approach to short-term trafficforecasting, European Journal of Operational Research, Vol. 131, No. 2, 253-261.

Ikeda, H. (2004) Personal Communication .On database technologies, Vogiatzis, N.

Lew, Yii-Der & Tee, Celeste Lian-Yong (2000) – Singapore’s Experience with Road Pricing: From Manual to Electronic – Technical Report at the 5th

ASEAN- Japan Workshop-cum-seminar on Urban Transportation.

Menon, A P G & Chin, Kian-Keong (1998) – The Making of Singapore’s Electronic Road Pricing System – Proceedings of the International Conference on Transportation into the next Millennium, Singapore.

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