Ads: Get Admission into 200 Level and Study any Course in any University of Your Choice. Low Fees | No JAMB UTME. Call 09038456231

Development of a Traffic Light Controller Model using Artificial Bee Colony Based Adaptive Dynamic Scheduling Algorithm

ADS! Obtain Up to N300,000 Cash in the 2020 Aspire Contest

Development of a Traffic Light Controller Model Using Artificial Bee Colony Based Adaptive Dynamic Scheduling Algorithm.

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 the traffic conditions 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

  • Background………………………………………………………………………………………………………… 1
  • Intelligent Transportation System (ITS)………………………………………………………………….. 1
    • Advanced traffic light management System (ATMS)…………………………………………. 1
    • Advanced traveler information system, (ATIS)…………………………………………………. 2
    • ITS –enabled transportation pricing systems, (ITS-ETPS)………………………………… 2
    • Advanced public transportation system, (APTS)……………………………………………….. 2
    • Vehicle to infrastructure integration (VII) and Vehicle to vehicle (V2V) 2
  • Motivation……………………………………………………………………………………………………………… 3
  • Significance of the research……………………………………………………………………………………… 3
  • Statement of the problem………………………………………………………………………………………….3
  • Aim and Objectives…………………………………………………………………………………………………. 4
  • Methodology……………………………………………………………………………………………………………5
  • Outline…………………………………………………………………………………………………………………….5

CHAPTER TWO: LITERATURE REVIEW

  • Introduction……………………………………………………………………………………………………………… 7
  • Review of Fundamental Concepts……………………………………………………………………………….. 7
    • Terminologies used in intelligent transportation systems……………………………………….7
    • Wireless Sensor Network…………………………………………………………………………………….8
    • Sensing Technologies………………………………………………………………………………………….. 9
    • Overview of a road intersection 13
    • Computational Approaches to Traffic Light Optimization…………………………………… 15
    • Adaptive Dynamic Scheduling Algorithm 25
    • Traffic Congestion 26
    • Traffic Stream 26
    • Single Intersection Base Model Formulation 27
    • Traffic Control on Multiple Intersection (TCAMI) 29
    • Approaches used in Programming Traffic Signals 29
  • Review of Similar Works 31

CHAPTER THREE: MATERIALS AND METHODS

  • Introduction 39
  • Mathematical Model of Vehicular Traffic Control System (VTCS)……………………………………………………. 39
    • Developed Intersection Model 42
  • Traffic Phases 46
  • Artificial Bee Colony (ABC) Algorithm based Vehicular Traffic Control System………………………………….51
  • Developed Vehicular Traffic Control Algorithm (VTCA) 55
  • Vehicular Traffic Control Simulator 60
  • Mode of Communication between Wireless Sensor Detectors and ABC Algorithm……………………………..63

CHAPTER FOUR: RESULTS AND DISCUSSION

  • Introduction 63
  • Simulation 63
    • Scenario 1: Constant Average Arrival Rate (AAR) and Departure (ADR)……………………………….. 65
    • Scenario 2: Variable Average Arrival Rate (AAR) and Departure (ADR)………………………………… 67
    • Scenario 3………………………………………………………………………………………………………………………… 68
  • Validation…………………………………………………………………………………………………………………………………. 74

CHAPTER FIVE: CONCLUSION AND RECOMMENDATION

  • Conclusion………………………………………………………………………………………………………………………………. 77
  • Significant Contributions………………………………………………………………………………………………………….. 77
  • Limitations………………………………………………………………………………………………………………………………. 78
  • Recommendation for Further Work……………………………………………………………………………………………. 78

REFERENCES……………………………………………………………………………………… 80

INTRODUCTION

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).

REFERENCES 

Alam, J. & Pandey, M. (2014). Advance Traffic Light System based on Congestion Estimation using Fuzzy Logic. International Journal of Emerging Technology and Advanced Engineering. (IJETAE), 5(1), 38-43

Aljaafreh, A. & Oudat, N. (2014).Optimized Timing Parameters for Real-time Adaptive Traffic signal Controller. 16th International Conference on Computer Modelling and Simulation, 243-247.

Anokye, M., Abdul-Aziz, A., Annin, K., & Oduro, F. T. (2013). Application of Queuing Theory to Vehicular Traffic at Signalized Intersection in Kumasi-Ashanti Region, Ghana. American International Journal of Contemporary Research, 3(7).

Arrobo, G. (2012).Improving the throughput and reliability of wireless sensor networks with application to wireless body area networks (Unpublished doctoral dissertation). P2. Retrievedfromhttp://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=5475&context=etd Collotta, M., Bello, L. L., & Pau, G. (2015). A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple fuzzy logic controllers. Expert Systems with Applications, 42(13), 5403-5415.

CSN Team.

Enter your email address:

Delivered by TMLT NIGERIA

Join Over 3,500 000+ Readers Online Now!


=> FOLLOW US ON INSTAGRAM | FACEBOOK & TWITTER FOR LATEST UPDATES

ADS: KNOCK-OFF DIABETES IN JUST 60 DAYS! - ORDER YOURS HERE

COPYRIGHT WARNING! Contents on this website may not be republished, reproduced, redistributed either in whole or in part without due permission or acknowledgement. All contents are protected by DMCA.
The content on this site is posted with good intentions. If you own this content & believe your copyright was violated or infringed, make sure you contact us at [[email protected]] to file a complaint and actions will be taken immediately.

Tags: , , ,

Comments are closed.