dynamic fuzzy logic traffic light integrated system …

DYNAMIC FUZZY LOGIC TRAFFIC LIGHT

INTEGRATED SYSTEM WITH ACCIDENT

DETECTION AND ACTION

BY

ABDULRAHMAN ABDULLAH ALKANDARI

A thesis submitted in fulfillment of the requirement for the

degree of Doctor of Philosophy (Computer Science)

Kulliyyah of Information and

Communication Technology

International Islamic University Malaysia

AUGUST 2014

ii

ABSTRACT

The increase of vehicular traffic in the cities is a major concern for the Traffic

Management Systems worldwide. The way traffic flow is regulated in the cities

directly or indirectly affects the citizen’s life. Thus there is need to optimize and

regulate the flow of the traffic effectively to meet the ever increasing demand. This

research takes an innovative approach in solving the congestion related to the

vehicular traffic, firstly by minimizing the wait time for the vehicles at traffic lights

depending on the volume of the traffic and secondly by devising a solution to

determine the exact location of the roadblock (caused by an accident or a vehicle

breakdown). For the first part of the research, it is vital to study and compare the

existing algorithms of the Traffic Control system and overcome their shortcomings.

One of the key parameters on which the research is based on is the Cross Ratio (i.e.

the number of cars that cross the signal per second). The cross ratio helps to decide

the effectiveness of the algorithms so it can support in any case of vary on flow of

traffic. The most significant result of the study is the proposed Dynamic Webster with

Dynamic Cycle time method (DWDC), which resulted in the largest total Crossed Car.

The second part of the research focuses on strategically placing the sensors and

sending real time traffic flow data into the Traffic management system connected

through an internal network. Our optimal algorithm supported by Fuzzy Logic to

control and detect an accident on the traffic lights in real time. This research

accomplishes an intelligent dynamic traffic light system for optimally controlling the

traffic flow and accident detection in cities by its contributions in the proposed two-

layer framework namely the architecture layer and the application layer. The highest

result of the proposed algorithm (DWDC) showed a significant improvement of

98.28% of total crossed car ratio compared with previous methods, namely Dynamic

Webster 93.23%, Webster 93.07%, Optimum equal intervals (Optimum Fixed Time)

92.64% and Equal Intervals (Fixed Time) 84.57% using iTraffic software. The

location of the accident is also proved to be precise giving the exact lane (Section) and

the block (Zone) of the affected car using FuzzyTech Program and the input of

average traffic light cross ratio 2 sec was taken in real life scenarios from video

recorded. The system also takes into account the possibility of false alarms. It respects

the fact that algorithms, no matter how precise, might make mistakes. Testing the

system resulted in a staggering 96% incident detection rate and only a 4% false alarm

rate. The action system proposed has demonstrated a major rise of percentage total

crossed car with 9.32% compared with DWDC, also intuitive to take an appropriate

action to solve the congestion on the accident road compared with DWDC in accident

scenarios that used iTraffic software.

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Cross Ratio

Cross Ratio

Dynamic Webster with

Dynamic Cycle time

Fuzzy Logic

DWDC

Dynamic Webster%93.23 (%93.07

Webster)92.64 (%84.57iTraffic

Fuzzy Tech

DWDC

DWDCiTraffic

iv

APPROVAL PAGE

The thesis of Abdulrahman Abdullah Alkandari has been approved by the following:

_____________________________________

Imad Fakhri Al-Shaikhli

Supervisor

_____________________________________

Abu Osman Bin Md Tap

Internal Examiner

_____________________________________

Nabil Kartam

External Examiner

_____________________________________

Fayez Gebali

External Examiner

_____________________________________

Abdul Kabir Hussain Solihu

Chairman

v

DECLARATION

I hereby declare that this dissertation is the result of my own investigations, except

where otherwise stated. I also declare that it has not been previously or concurrently

submitted as a whole for any other degrees at IIUM or other institutions.

Abdulrahman Abdullah Alkandari

Signature Date………17/7/2014.....……

vi

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION

OF FAIR USE OF UNPUBLISHED RESEARCH

Copyright © 2014 by Abdulrahman Abdullah Alkandari. All rights reserved

DYNAMIC FUZZY LOGIC TRAFFIC LIGHT INTEGRATED

SYSTEM WITH ACCIDENT DETECTION AND ACTION

No parts of this unpublished research may be reproduced, stored in a retrieval system, or

transmitted, in any form or by any means, electronic, mechanical, photocopying,

recording or otherwise without prior written permission of the copyright holder except

provided below.

1. Any material contained in or derived from unpublished research may only be

used by others in their writing with due acknowledgement

2. IIUM or its library will have the right to make and transmit copies ( print or

electronic) for institutional and academic purposes

3. The IIUM library will have the right to make, store in a retrieval system and

supply copies of this unpublished research if requested by other universities

and research libraries.

Affirmed by Abdulrahman Abdullah Alkandari

………………………… ……17/7/2014……

Signature Date

vii

ACKNOWLEDGMENTS

There are many people who have helped me accomplished this research endeavour.

However, among the many, there are few people whom their helps and assistances

were so great and that they should be appreciated by mentioning their names here.

I would like to thank my supervisor, Dr. Imad Fakhri Al-Shaikhli who gave

me great help and support with my PhD thesis. His valuable involvement and

supervision over the past few years have polished my skills and guided me through the

maze of research.

Special thanks and appreciation also goes to Assistant researcher Anas Najaa

for helping me in the implementation and design.

Particular thanks and gratitude also goes to Professor Dr. Yasser Hawas for the

comments and feedback or other forms of help.

I would also like to pay my thanks to my software developer Eng. Jamal

Alqabandi for his constructive comments and hard work developing and building a

custom micro-program (iTraffic) during the stage of refining my research framework.

I am grateful to my mother for her love and for being such a great source of

inspiration and motivation. Finally, I would like to thank my wife, Nourah and my

beloved son and daughter, Abdullah and Noor for the love, patience and support that

they gave me throughout my PhD. They made my PhD experience exceptional.

viii

TABLE OF CONTENTS

Abstract .................................................................................................................... ii

Abstract in Arabic .................................................................................................... iii

Approval Page .......................................................................................................... iv

Declaration ............................................................................................................... v

Copyright Page ......................................................................................................... vi

Acknowledgements .................................................................................................. vii

List of Tables ........................................................................................................... xi

List of Figures .......................................................................................................... xiii

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

1.1. Introduction ........................................................................................... 1

1.2. Problem Statement ................................................................................ 3

1.3. Scope ..................................................................................................... 4

1.4. Hypothesis ............................................................................................. 5

1.5. Questions ............................................................................................... 5

1.6. Objectives .............................................................................................. 6

1.7. Research Methodology.......................................................................... 7

1.8. Limitations and Constrains ................................................................... 14

1.9. Signifiance of Research......................................................................... 15

1.10. Flow of Thesis ..................................................................................... 15

CHAPTER TWO LITERATURE REVIEW ...................................................... 19

2.1. Introduction ........................................................................................... 19

2.2. Previous Researches .............................................................................. 19

2.3. Summary ............................................................................................... 53

CHAPTER THREE: THEORTICAL BACKGROUND ................................... 54

3.1. Introduction ........................................................................................... 54

3.2. The Traffic Management System .......................................................... 55

3.3. Intelligent Traffic Light Control System............................................... 56

3.3.1. Traffic Light Controllers Methods .............................................. 60

3.3.2. Webster’s Formula ...................................................................... 62

3.4. Advanced Traffic Light Control Systems (ATCS) ............................... 63

3.4.1. Types of ATCS ............................................................................ 64

3.5. Artificial Intelligence (AI) Techniques ................................................. 64

3.5.1. Fuzzy Logic ................................................................................. 65

3.6. Other Solutions For Itlms ...................................................................... 67

3.7. Traffic Control Algorithms (Methods).................................................. 69

3.7.1. Equal Interval (Fixed Time) ........................................................ 71

3.7.2. Optimum Equal (Optimum Fixed Time)..................................... 72

3.7.3. Webster ....................................................................................... 75

3.7.4. Dynamic Webster ........................................................................ 76

3.8. Boolean Logic Background................................................................... 77

3.8.1. Set Theory ................................................................................... 78

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3.8.2. Fuzzy Sets ................................................................................... 79

3.8.3. Fuzzy Set Operations .................................................................. 81

3.9. Fuzzy Logic Review ............................................................................. 84

3.9.1. Definition of Fuzzy Logic ........................................................... 85

3.9.2. Why Use Fuzzy Logic ................................................................. 86

3.9.3. Universe Of Discourse ................................................................ 87

3.9.4. Membership Functions ................................................................ 88

3.9.5. Fuzzy Rules (If-Then Rules) ....................................................... 91

3.9.6. Fuzzy Inference Systems (Fuzzy Rules Processing) ................... 94

3.10. Summary ............................................................................................. 95

CHAPTER FOUR: THE PROPOSED SYSTEM (PHYSICAL LAYER) ...... 98

4.1. Introduction ........................................................................................... 98

4.2. Physical Layer Layout........................................................................... 99

4.3. Hardware Component ........................................................................... 102

4.4. Software ................................................................................................ 102

4.5. Communication ..................................................................................... 102

4.6. Detection ............................................................................................... 103

4.7. Proposed Traffic Signal Controller Phase Flow.................................... 104

4.8. Summary ............................................................................................... 105

CHAPTER FIVE: THE PROPOSED SYSTEM (APPLICATION LAYER) . 106

5.1. Introduction ........................................................................................... 106

5.2. The Proposed System (Application) ..................................................... 107

5.2.1. Dynamic Webster with Dynamic Cycle Time ............................ 109

5.2.2. Accident Detection/Action Physical Communication ................ 110

5.2.3. Accident Detection System ......................................................... 112

5.2.4. Accident Action System .............................................................. 118

5.3. Fuzzy Logic System Components ......................................................... 126

5.3.1. Linguistic Variables .................................................................... 128

5.3.2. Membership Functions ................................................................ 138

5.4. Summary ............................................................................................... 156

CHAPTER SIX: RESULTS AND DISSCUSION .............................................. 157

6.1. Introduction ........................................................................................... 157

6.2. Implementation the Proposed Method (DWDC) .................................. 157

6.2.1. Parameters ................................................................................... 157

6.2.2. Definitions Used ......................................................................... 159

6.2.3. System Outputs ........................................................................... 160

6.2.4. Variables ..................................................................................... 161

6.2.5. The Decisive Factor .................................................................... 161

6.2.6. Other Factors ............................................................................... 162

6.2.7. Simulation Graphs ....................................................................... 163

6.3. Experimental Result on Signalized Intersection for One Phase

(Real Life) .................................................................................................... 171

6.4. Accident Detection And Action Using Fuzzy Tech .............................. 172

6.4.1. Fuzzy Tech Map .......................................................................... 172

6.4.2. Complete List of Inputs, Outputs and Intermediates in Fuzzy

Tech ..................................................................................................... 174

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6.4.3. Quick Review of Zone Status and Section Status Spreadsheet

Rules ..................................................................................................... 175

6.4.4. Scenario 1: ................................................................................... 177

6.4.5. Scenario 2: ................................................................................... 179

6.4.6. Scenario 3: ................................................................................... 181

6.5. Using Itraffic to Measure False Alarm Rate ......................................... 183

6.5.1. Rules Introduction ....................................................................... 183

6.5.2. Accident Status Rule Block ........................................................ 183

6.5.3. Accident Detection System Rule Block ...................................... 184

6.5.4. Accident’s Zone Rule Block ....................................................... 185

6.5.5. Accident’s Section Rule Block ................................................... 186

6.5.6. Downstream Rule Block ............................................................. 187

6.6. Accident Action System Using Itraffic ................................................. 191

6.6.1. Scenario 1 .................................................................................... 194

6.6.2. Scenario 2 .................................................................................... 196

6.6.3. Scenario 3 .................................................................................... 199

6.6.4. Scenario 4 .................................................................................... 201

6.7. Summary ............................................................................................... 202

CHAPTER SEVEN: CONCLUSION AND FUTURE WORKS ....................... 204

REFERENCES ....................................................................................................... 206

APPENDIX A (ITRAFFIC PROGRAM) ................................................................ 213

APPENDIX B (FUZZYTECH SOFTWARE) ......................................................... 233

APPENDIX C (SENSORS TYPES) ........................................................................ 246

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LIST OF TABLES

Table No. Page No.

2.1 Summary of literature review 50

3.1 Controller method comparison (Koonce, et al., 2008) 61

3.2 Logic functions 77

3.3 Fuzzy functions 81

5.1 Abbreviations for roads 129

5.2 Inputs and output of FL system 131

5.3 Red traffic light (cross ratio) 133

5.4 Green traffic light (cross ratio) 134

5.5 Red traffic light (zone status 1) 135

5.6 Red traffic light (zone status 2) 136

5.7 Section speed 137

5.8 Zones and lanes with accident 138

5.9 Cross ratio fuzzy terms 139

5.10 Gap filling time 141

5.11 Zone status demo 2 141

5.12 Zone status fuzzy terms 142

5.13 Section speed fuzzy terms 143

5.14 Accident terms fuzzy terms 145

5.15 Rules of accident status 153

5.16 Rules of accident status 2 155

6.1 Parameters of simulation 158

6.2 Inputs of FuzzyTech. 174

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6.3 Intermediates of FuzzyTech. 174

6.4 Outputs of FuzzyTech. 174

6.5 No accident default road. 175

6.6 No accident default road 2. 175

6.7 Accident enabled default road. 176

6.8 Accident enabled default road 2. 176

6.9 Accident enabled default road 3. 176

6.10 All the rules at a glance 187

6.11 Input selection in iTraffic program for action system 192

6.12 The improvement for action system in all scenarios 193

6.13 Comparison for percentage of action system improvement (Normal

road vs. Normal road with accident) 196

6.14 Comparison of percentage of action system improvement (Normal

road vs. Traffic road with accident) 198

6.15 Comparison of percentage of action system improvement (Traffic

road vs. Normal road with accident) 200

6.16 Comparison of percentage of action System improvement (Traffic

road vs. Traffic road with accident) 202

xiii

LIST OF FIGURES

Figure No. Page No.

1.1 Research process and methodology 8

1.2 The proposed system map 10

1.3 The proposed system (Physical) 11

1.4 The proposed system top level (Application) 13

3.1 Basics of phase (Jraiw, 2003) 56

3.2 Phase diagram for all red and without all red (Jraiw, 2003) 58

3.3 Effective green time (Jraiw, 2003) 59

3.4 Traffic controller methods 60

3.5 Working fuzzy logic 65

3.6 Basic of Petri Nets (Di Febbraro, Giglio and Sacco, 2004) 68

3.7 Equal interval flow chart 71

3.8 Optimum equal flow chart 72

3.10 Dynamic Webster flow chart 76

3.11 Compliment 82

3.12 Intersection graph 83

3.13 Union graph 84

3.14 Intersection between sets 88

3.15 Triangular MF 89

3.16 Trapezoidal MF 90

3.17 Cross over point 91

3.18 fuzzy rule flow 93

4.1 Physical layout for system diagram 100

4.2 Architecture of proposed system 101

xiv

4.3 Detector and zone 103

4.4 Green to red 104

4.5 Red to green 105

5.1 The proposed system (detailed) 107

5.2 Dynamic Webster Dynamic Cycle time flow chart 109

5.3 Road diagram 111

5.5 Phase 1 ADS 114

5.6 Gap Filling Time Identification Process 115

5.7 Phase 2 ADS 117

5.8 Phase 3 ADS 118

5.9 Road block 119

5.10 Comparison between DWDC and normal traffic light 120

5.11 DWDC algorithm effect on road 121

5.12 Accident without FL 122

5.13 Accident with FL 122

5.14 Downstream action diagram 123

5.15 Road block2 124

5.16 Upstream action system 125

5.17 Upstream diagram 126

5.19 Linguist vs. numbers 129

5.20 Zones, lanes and section 132

5.21 Cross ratio demo 1 133

5.22 Cross ratio demo 2 133



5.25 Section speed 137

5.26 Cross ratio membership function 140

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5.29 Zone status membership function 142

5.30 Section speed 1 144

5.31 Section speed 2 145

5.32 Accident status MF 146

5.33 Zones demo 151

5.34 Zones demo 2 152

6.1 Simulation results 163

6.2 Cycle time 60 seconds 166





6.7 Cycle time210 seconds 170


6.9 Average time of all the rows in their perspective record. 171

6.10 Fuzzy tech map 173

6.11 Inputs and outputs of scenario 1. 177

6.12 3D plot of scenario 1. 178

6.13 Inputs and outputs of Scenario 2. 179

6.14 3D plot of scenario 2. 180

6.15 Inputs and Outputs of scenario 3. 181

6.16 3D Plot of scenario 3 182

6.17 Accident generator options in iTraffic 188

6.18 Car accident in iTraffic simulation 189

6.19 Car accident 2 in iTraffic simulation 190

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6.20 iTraffic options for action system 192

6.21 iTraffic with action system enabled 193

6.22 Comparison for total car crossed (Normal road vs. Normal road with

accident) 195

6.23 Comparison for total car crossed (Normal road vs. Traffic road with

accident) 197

6.24 Comparison for total car crossed (Traffic road vs. Normal road with

accident) 199

6.25 Comparison for total car crossed (Traffic road vs. Traffic road with

accident) 201

1

CHAPTER ONE

INTRODUCTION

1.1. INTRODUCTION

Ever since Kevin Ashton first used Internet of Things in 1999 there has been a surge

in research field on how our daily life can be optimized by the use of intelligent

system and facilitated the idea of Smart Cities (Weber, 2009).

Primarily Smart City is a concept revolving around its core components

namely citizens, infrastructure, government, technology providers, research

companies; exchange information on the fly for taking informed decisions for living a

better life. A smart city is a self-contained town in terms of evolution on Information

and Communication Technology (ICT) infrastructure. A modern-day city comprises

of intelligent answers to ease the organization of daily life. In order to achieve these

sensors play a very important role in receiving, processing, analyzing and

retransmitting the data. This is the core concept followed by any ICT - intensive

solutions - which makes it popular in many models for urban development.

One of the fundamental building blocks of Smart Cities is Transport

Management System. As cities develop, there is a great influx of people in the city,

which is directly proportional to the number of vehicles on the road. This calls for

Traffic Management System (TMS), which can effectively control the flow of traffic.

TMS is an innovative design for the road that saves time and money for the driver.

This solution creates a city of intelligence, which can be controlled automatically

through sensors. TMS typically works in isolation, as it does not share information. A

prearranged procedure guides the system to operate at different times throughout the

2

day. Typical scenarios are morning and evening peak hours and off peak hours. The

inherent drawback of the system is they are very static and cannot be flexible to

correspond to the change of traffic demands as they assume a constant flow of traffic.

Accident detection is not possible and thus corresponding changes cannot take place

(Nigarnjanagool & Dia, 2005).

A variety of different control systems are used to fulfill Traffic Light

Management such as: SYNCHRO or TSIS. These systems have weakness on dynamic

and real time for solving the traffic light issues. They calculate the result for the time

of input data without controlling the traffic light system dynamically. They work on

software platform without any hardware connection like sensors and routers (Zhao &

Tian, 2012).

Traffic congestion is a major and growing issue facing urban cities. With the

growth of cities and the growth of economic activity where the population density was

increasing, which leads to increasing the flow rate of vehicle traffic on the roads.

Thus, exacerbate the congestion while increasing the probability of accidents. That

requires solutions to reduce these problems (Transport & Centre, 2007).

Adaptive Traffic Control Systems (ATCSs) are a relatively new method on

urban signal control systems; research began in the 1970's (Shenoda, 2006). ATCSs

optimize signal-timing parameters to reduce traffic delays by using real-time traffic

data. The primary ATCS systems are SCOOT, SCATS, OPAC, RHODES, and ACS

Lite. SCOOT, SCATS, OPAC, and RHODES they are more demanding for

operationally, in addition they require high maintenance for detectors or

communications (Zhao & Tian, 2012). These systems considered expensive and

complicated compared to traditional traffic signal systems. When traffic demand

3

exceeds the capacity, these systems cannot work efficiently enough, and this is a

drawback for these ATCSs (LI, et.al, 2013).

Many artificial intelligence techniques, such as Reinforcement Learning,

Fuzzy Logic, Expert Systems, Genetic algorithm, Swarm algorithm, Rule-based

systems and Neural Network have been used in traffic lights systems in order to

improve these systems and make them more capable of controlling traffic lights

(Wiering, et.al, 2004).

Traffic management systems (TMS) have given adequately controlling for the

flow of traffic by applying the information and communication technology in the

transport sector. TMS has provided solutions for the traffic-congestion by managing

the traffic on the roads and highways and improving the flow of traffic, which

contributes to saving time and money for the driver (Veenswijk, et.al, 2012). The

disadvantage of TMS is they assume a constant flow of traffic, also they are static and

cannot be adapt to the traffic change. In addition, it does not share information and

that because it usually works in isolation (Nigarnjanagool & Dia, 2005).

The effectiveness of the control systems in traffic can be determined by its

ability to adapt to changes. As long as the adaptability is part of the traffic-control

unit, and these systems are able to optimize and adjust the signal settings, they would

interact better with the changes in traffic conditions, improve the vehicular throughput

and minimize delay (Roozemond & Rogier, 2000).

1.2. PROBLEM STATEMENT

The problem statement focuses on multiple aspects of the traffic light control system.

The thesis addresses the following problems either individually or collectively the

following statements:

4

i. The systems in focus are static and not adaptive.

ii. The systems in focus are adaptive but there is no accident detection in

place.

iii. The systems are adaptive and capable of detecting the accidents but they

lack the mechanism of sensing the exact location and taking the necessary

actions.

1.3. SCOPE

The main purpose of this research is to provide solutions for the growing traffic

congestion problem, which is facing the urban cities. This congestion causes many

problems and challenges that call an improvement of current methods to control the

traffic. The study covers an isolate traffic light in a city as an experimental test by

camera record. Implementing software using visual basic and My SQL will compare

between proposed algorithm and existing methods and to discuss the flow of the

traffic. The methods studied and were part of scope included Fixed Time, Optimum

Fixed Time, Webster and Dynamic Webster. Many artificial intelligence techniques

were part of scope of the research to develop the traffic control algorithm used to

calculate the optimal time for flow rate of the traffic across the intersection.

The scope of the study covered the traffic management system (TMS),

intelligent traffic light control system and the artificial intelligence (AI) techniques.

This research focuses on the improvement areas for the existing traffic light system. It

covers the proposed system that improves the system performance and reduces traffic

delays, and its architecture. In addition, this study presents design and implement for

the proposed new algorithm. The study involved a comparison between the proposed

algorithm and existing methods. Fuzzy logic has been discussed in detail, and it is

5

supporting the system to detect the exact location of the incidents and take

corresponding action.

Two main programs have been used in the study namely: FuzzyTech program

and iTraffic program. FuzzyTech program is an easy readymade-Program to give the

exact result for different scenarios. iTraffic program is an open source and custom

micro-program; it used to give real time data. The research was finally based on a

fuzzy logic theory for accident detection. The scope also included a real life study of

a congestion traffic intersection. The main limit of this study, there was no actual

implementation of the proposed system on reality, and it can be used only on one-

phase traffic intersections.

1.4. HYPOTHESIS

i. It is hypothesized that the existing traffic light management system

methods are lack of dynamic saturation flaw along with dynamic cycle

time.

ii. It is hypothesized that, by using the available incident detection systems,

the traffic management system cannot detect the exact location of the

breakdown vehicle.

iii. It is hypothesized that the available traffic light management systems, do

not provide a solution for a congestion caused by incidents.

1.5. QUESTIONS

The following research questions are formulated and will set the direction of this

research:

6

i. What are the limitations of the existing traffic light management system

methods?

ii. Can we design the dynamic intelligent traffic light system to be adaptive,

capable of detecting an incident and making the reaction?

iii. What is the appropriate AI technique for the algorithm of traffic light

management system?

iv. How does inclusion of fuzzy logic enhance the traffic light management

system?

v. How can we improve the accuracy (decrease false alarm rate, increase

incident detection rate, and cross ratio) of the intelligent traffic light

management system?

1.6. OBJECTIVES

The following points display the objectives of the research and drive the efforts of the

study:

i. To showcase the dynamic intelligent traffic light system to be adaptive,

capable of detecting an incident and making the reaction, comparing with

existing traffic light system.

ii. To lay out a feasible design of the traffic light with the proposed

infrastructure with hierarchical dynamic intelligent traffic light system.

iii. To compare the existing methods with the new proposed algorithm and to

prove the effectiveness of the proposed algorithm using the number of

crossed cars as measurement.

iv. To design and implement an optimal algorithm supported by Fuzzy Logic

for detecting exact location of accidents and take corresponding action.

7

v. To showcase the calculation and importance of cross ratio and to measure

the accuracy of the system by measuring false alarm rate and incident

detection rate using the experimental simulation software (iTraffic).

1.7. RESEARCH METHODOLOGY

The studies carried out in this research have been focused on the two main areas

namely the architecture layer and the application layer. The research methodology

encompasses the efforts made in each of them.

Studies revolving around the architecture layer were focused more on the

Qualitative research which concentrated on case studies, studying and evaluating the

vendor datasheets for the various hardware components, and conducting feasibility

study to choose the most appropriate hardware. The used case scenarios were built to

emulate the data flow in a traffic management system to select the best routing

protocol for the traffic management system and showcasing theoretical comparison of

the routing protocol. Details of the metrics and parameters will be discussed in the

chapter physical layer.

The heart of the thesis focuses on the new proposed system to be used in the

traffic light system. The main of the contributions are:

i. The proposed algorithm, Dynamic Webster with dynamic Cycle Time, to

optimize the flow of the traffic and to improve the total crossed car.

ii. Accident Detection System using fuzzy logic theory: Fuzzy logic

technique is used to design and implement an optimal algorithm

depending on cross ratio, zones, and sections (lanes) for detecting exact

location of incidents.

8

iii. Action System depending on Detection System: The Action System

provides an improvement performance in the accident road when one or

more of other roads have traffic.

The methodology followed revolves around development and testing the new

algorithm. The steps are as shown in figure 1.1:

Define

Research

Problem

Research

Design /

Building a

Prototype

Data

Collection

and

Analysis

Interpret

and

Feedback

Documentation

Review

Concept

Review

Theories

and

Research

Comparative

Study and

Research

Analysis

Figure11.1 Research process and methodology

STEP 1: Formulating the research problem.

The formulation of this thesis is based on the empirical and conceptual studies made

in the field of Intelligent Traffic Light Control Systems as part of Traffic Engineering.

Research objectives and Significance of work play a major role in formulating the

Research Problem.

STEP 2: Extensive literature survey.

This step builds on the preliminary reviews done and do a detailed analysis of the

topic in scope in Academic journals, conference proceedings, government reports,

books and any other credible source in field of Smart Cities, Intelligent Traffic Light

Control Systems, Traffic Engineering, and AI techniques ( Fuzzy Logic, Expert

dynamic fuzzy logic traffic light integrated system …

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