Upload
others
View
2
Download
0
Embed Size (px)
Citation preview
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.
iii
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
ix
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
x
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
xi
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
xii
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
5.23 Zone status demo 1 135
5.24 Zone status demo 2 136
5.25 Section speed 137
5.26 Cross ratio membership function 140
xv
5.27 Zone status demo 1 140
5.28 Zone status demo 2 141
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
6.3 Cycle time 90 seconds 166
6.4 Cycle time 120 seconds 167
6.5 Cycle time 150 seconds 168
6.6 Cycle time 180 seconds 169
6.7 Cycle time210 seconds 170
6.8 Cycle time 240 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
xvi
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