ABSTRACT
An Adaptive Dynamic Scheduling Algorithm (ADSA) based on Artificial Bee Colony (ABC) was developed for vehicular traffic control. The developed model optimally schedule green light timing in accordance with traffic condition in order to minimize the Average Waiting Time (AWT) at the cross intersection. A MATLAB based Graphic User Interface (GUI) traffic control simulator was developed. Three scenarios of vehicular traffic control were simulated and the results and the presented results shows that scenario one and two demonstrated the variation of the AWT and Performance of the developed algorithm with changes in the maximum allowable green light timing over the simulation interval. In the third scenario, an AWT of 38sec was recorded against a maximum allowable green light duration of 120sec, during which 1382 vehicles were evacuated from the intersection, leaving 22 vehicles behind. The algorithm also had a performance of 98.43% over a simulation duration of 1800sec. In order to demonstrate the effectiveness of the developed ADSA this research was validated with the literature. The result obtained for the AWT of the developed ADSA had a performance of 76.67%. While, for vehicular queues cleared at the intersection the developed ADSA had a performance of 53.33%. The results clearly expressed that the developed ADSA method has been successful in minimizing the Average Waiting Time and vehicular queues at intersection.
TABLE OF CONTENTS
TITLE PAGE. …………………………………………………………………………………………………………………. i
DECLARATION …………………………………………………………………………………………………………….. i
CERTIFICATION ………………………………………………………………………………………………………….. ii
DEDICATION ………………………………………………………………………………………………………………. iii
ACKNOWLEDGEMENT ………………………………………………………………………………………………. iv
ABSTRACT ………………………………………………………………………………………………………………….. vi
LIST OF FIGURES ……………………………………………………………………………………………………….. ix
LIST OF TABLES ………………………………………………………………………………………………………… xii
LIST OF ABBREVIATIONS ………………………………………………………………………………………… xiii
CHAPTER ONE: GENERAL INTRODUCTION
1.1 Background …………………………………………………………………………………………………………… 1
1.2 Intelligent Transportation System (ITS) ……………………………………………………………………. 1
1.2.1 Advanced traffic light management System (ATMS) …………………………………………… 1
1.2.2 Advanced traveler information system, (ATIS)……………………………………………………. 2
1.2.3 ITS –enabled transportation pricing systems, (ITS-ETPS) …………………………………….. 2
1.2.4 Advanced public transportation system, (APTS) ………………………………………………….. 2
1.2.5 Vehicle to infrastructure integration (VII) and Vehicle to vehicle (V2V) integration. . 2
1.3 Motivation ………………………………………………………………………………………………………………… 3
1.4 Significance of the research ………………………………………………………………………………………… 3
1.5 Statement of the problem ……………………………………………………………………………………………. 3
1.6 Aim and Objectives …………………………………………………………………………………………………… 4
1.7 Methodology …………………………………………………………………………………………………………….. 5
1.8 Outline …………………………………………………………………………………………………………………….. 5
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CHAPTER TWO: LITERATURE REVIEW
2.1 Introduction ………………………………………………………………………………………………………………. 7
2.2 Review of Fundamental Concepts ……………………………………………………………………………….. 7
2.2.1 Terminologies used in intelligent transportation systems ………………………………………. 7
2.2.2 Wireless Sensor Network ………………………………………………………………………………….. 8
2.2.3 Sensing Technologies ………………………………………………………………………………………. 9
2.2.4 Overview of a road intersection ……………………………………………………………………….. 13
2.2.5 Computational Approaches to Traffic Light Optimization …………………………………… 15
2.2.6 Adaptive Dynamic Scheduling Algorithm ………………………………………………………… 25
2.2.7 Traffic Congestion ………………………………………………………………………………………… 26
2.2.8 Traffic Stream ………………………………………………………………………………………………. 26
2.2.9 Single Intersection Base Model Formulation ……………………………………………………. 27
2.2.10 Traffic Control on Multiple Intersection (TCAMI) ……………………………………………. 29
2.2.11 Approaches used in Programming Traffic Signals …………………………………………….. 29
2.3 Review of Similar Works …………………………………………………………………………………………. 31
CHAPTER THREE: MATERIALS AND METHODS
3.1 Introduction …………………………………………………………………………………………………………….. 39
3.2 Mathematical Model of Vehicular Traffic Control System (VTCS) ……………………………….. 39
3.2.1 Developed Intersection Model …………………………………………………………………………….. 42
3.3 Traffic Phases …………………………………………………………………………………………………………. 46
3.4 Artificial Bee Colony (ABC) Algorithm based Vehicular Traffic Control System …………… 51
3.5 Developed Vehicular Traffic Control Algorithm (VTCA) …………………………………………….. 55
3.6 Vehicular Traffic Control Simulator …………………………………………………………………………… 60
3.7 Mode of Communication between Wireless Sensor Detectors and ABC Algorithm………..63
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1 Introduction …………………………………………………………………………………………………………….. 63
4.2 Simulation ………………………………………………………………………………………………………………. 63
4.2.1 Scenario 1: Constant Average Arrival Rate (AAR) and Departure (ADR) ………………… 65
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4.2.2 Scenario 2: Variable Average Arrival Rate (AAR) and Departure (ADR) ………………… 67
4.2.3 Scenario 3…………………………………………………………………………………………………………. 68
4.3 Validation ……………………………………………………………………………………………………………….. 74
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION
5.1 Conclusion ……………………………………………………………………………………………………………… 77
5.2 Significant Contributions ………………………………………………………………………………………….. 77
5.3 Limitations ……………………………………………………………………………………………………………. 788
5.4 Recommendation for Further Work ………………………………………………………………………….. 788
REFERENCES………………………………………………………………………………….80
LIST OF APPENDICES
APPENDIX A
Sub-M-Files for Artificial Bees Colony Algorithm…………………………….…………..86
APPENDIX B
Main M-Files ABC Algorithm…………………………………………………………..…..90
APPENDIX C
Objective Function for Traffic………….……………………………………………..……..92
APPENDIX D
Objective Function for ABC………………..……………………………………………..…93
CHAPTER ONE
GENERAL INTRODUCTION
1.1 Background
Intelligent Transportation System (ITS) is a system in which information and communication technologies are applied in road transport, including infrastructure, vehicles and users, and in traffic management and mobility management, as well as for interfaces with other modes of transportation (Kotwal et al., 2013). The Intelligent Transportation System (ITS) make use of technologies in electronics, communications, computers, control, sensing and detecting in all kinds of transportation system. The primary goals of ITS systems are to “increase transportation system efficiency and capacity such as: enhance mobility, improve safety, reduce energy and environmental costs” (Kotwal et al., 2013).
1.2 Intelligent Transportation System (ITS)
Generally, Intelligent Transportation Systems (ITS) are classified into five systems according to their functions as follows (Samadi et al., 2012):
1.2.1 Advanced traffic light management System (ATMS)
ATMS is usually employed to detect traffic situations, transmits them to a control center via communication network, and then develops traffic control strategies by combining all kinds of traffic information. The system makes use of base stations to carry out traffic control and transmits the information to drivers and concerned departments (Samadi et al., 2012). ATMS includes ITS applications that focus on traffic control devices such as ramp metering, adaptive traffic signal control, traffic operation centers (TOC) and high occupancy vehicle control and so on (Ezell, 2010).
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1.2.2 Advanced traveler information system, (ATIS)
This system allows road users to access real time information such as in the car, at home, in the office or outdoor by choosing transportation modes, travel trips and routes. The system mainly includes: changeable message signs, Highway Advisory Radio, Global Positioning System Road side weather information systems and so on (Ezell, 2010).
1.2.3 ITS –enabled transportation pricing systems, (ITS-ETPS)
Employing ITS-ETPS technologies to road network management helps drivers to automatically pay tolls electronically via a Dedicated Short Range Communications (DSRC-enabled onboard device) or tag placed on the windshield. Another ITS-enabled approach is Vehicles Miles Travelled (VMT) that charges motorists based on miles driven (Ezell, 2010). With this application ITS finds a central role in financing most countries transportation systems (Samadi et al., 2012).
1.2.4 Advanced public transportation system, (APTS)
APTS applies the technology of ATMS, ATIS and ITS-ETPS in public transportation in order to improve the quality of service, increase efficiency and the number of people who take public transportation (Samadi et al., 2012).The system mainly includes the automatic vehicle location (AVL) monitoring, computer scheduling and e–tickets (Ezell, 2010).
1.2.5 Vehicle to infrastructure integration (VII) and Vehicle to vehicle (V2V) integration.
Vehicle-to-vehicle applies the technology of Intelligent Speed Adaptation (ISA) which is aimed to assist drivers in keeping with speed limits. By correlating information about the vehicles position with a digital speed limit map thus enabling it to recognize if it has passed posted speed limit (Ezell, 2010). Also, Cooperative Intersection Collision Avoidance systems (CICAS) play a role in V2V integration systems (Samadi et al., 2012).
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Maintaining an efficient transportation system has been the main concern for public authorities in a city confronted with multiple traffic jams each day, which cause large parts of it to become irresponsive to traffic movement(s). Various studies have revealed that the major problem lies in the number of intersections and their coordination in terms of controlling (releasing or halting) the traffic which leads to traffic congestion (Cosariu et al., 2013). Many intelligent control systems have been developed and most of the researchers test these systems on simple networks with theoretical (and often static) traffic volumes (Sinhmar, 2012) and (Mohan & Rani,2013). This research work is set out to develop a traffic light controller model using an artificial bee colony based adaptive dynamic scheduling algorithm to minimize the average waiting time and conflict.
1.3 Motivation
The problems associated with fixed- time controllers which made use of pre-determined traffic control that does not respond to dynamic changes to traffic condition on each lane was the reason a new optimization algorithm known as the Artificial Bee Colony was introduced.
1.4 Significance of the research
To develop an adaptive traffic controller model using Artificial Bee Colony that is aimed at minimizing average waiting time and conflicts at vehicular intersections with emphasis on simultaneous moves.
1.5 Statement of the problem
The order and durations of the green lights for intersection traffic management are pre-determined and do not adapt dynamically to the traffic conditions. Detectors are sometimes used to count vehicles on each lane of an intersection but the data they report is generally used only to select between a few static sequences and timings setups. When unmanaged, high vehicle volume within urban traffic networks results in congested and slow moving traffic. This has led to many negative
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economic and environmental consequences (McKenney, 2011). It is therefore important to efficiently control road networks vehicles. Many researchers have made an effort to improve the efficiency of traffic signals using many intelligent systems evaluated using unrealistic traffic models which often include single intersection and static traffic volumes. The developed model for this work introduced a more realistic control strategy through the development of an adaptive dynamic scheduling algorithm based on an artificial bee colony algorithm that is capable of controlling the vehicular traffic at road intersections.
1.6 Aim and Objectives
The aim of the research is development of a traffic light controller model using an artificial bee colony (ABC) based adaptive dynamic scheduling algorithm. The objectives are:
1 To develop a model for vehicular traffic control system and implement an artificial bee colony based adaptive dynamic scheduling algorithm for road intersection using MATLAB R2012a Software.
2 To develop a Graphic User Interface (GUI) based vehicular traffic management simulator using MATLAB R2012a software.
3 To simulate various scenarios of traffic management using the developed simulator in (2) and compare the results of average waiting time with those obtained in the work
(Erwan et.al, 2015).
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1.7 Methodology
The following sequence of steps is adopted in order to achieve the aim and objectives of this research.
1. Development of mathematical model for a cross-junction, fitted with traffic signals and sensors to monitor traffic movement.
2. Development of a motion control strategy which comprised of twenty two possible phases and four simultaneous moves of the traffic at the intersection.
3. Implementation of a motion control strategy on the developed intersection in (1).
4. Development of an adaptive dynamic scheduling algorithm using artificial bee colony that optimally allocate time to the various traffic signals controlling traffic movement.
5. Development of a MATLAB GUI simulator to aid vehicular traffic control simulation based on the developed algorithm in (4).
6. Simulating various scenarios of traffic control to obtain results for comparison with those of the work of (Erwan et al., 2015) for validation purpose.
1.8 Outline
In Chapter one, general background on Intelligent Transportation System (ITS) was presented. In Chapter two, a concise review of the fundamental concepts and literature regarding Adaptive Traffic Control and various optimization algorithms are presented. In Chapter three, the developed model for Vehicular Traffic Control System was developed using Artificial Bee Colony Algorithm are presented respectively; MATLAB GUI are also developed and presented in the chapter. In Chapter four, Vehicular Traffic Control System was simulated using the developed simulator (presented in Chapter three); the results are presented are briefly discussed; and the developed simulator were further compared with the results obtained by (Erwan et al., 2015) as a means of
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validation. Chapter five presents the conclusion, recommendations, and limitations of the entire research work. The list of cited references and MATLAB codes in the appendices are provided at the end of this dissertation.
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