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

Description

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

Download Development of a Traffic Light Controller Model Using Artificial Bee Colony Based Adaptive Dynamic Scheduling Algorithm. Electrical and Computer Engineering students who are writing their projects can get this material to aid their research work.

Abstract

An Adaptive Dynamic Scheduling Algorithm (ADSA) based on Artificial Bee Colony (ABC) was developed for vehicular traffic control. The developed model optimally schedules 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 show 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.

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 the 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).

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