Development and Simulation of Adaptive Traffic Light Controller Using Artificial Bee Colony Algorithm

Abstract

This paper proposes an adaptive traffic control system that dynamically manages traffic phases and durations at cross-intersection. The developed model optimally schedules green light timing in accordance with traffic condition on each lane 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 presented. The 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. The algorithm also had a performance of 98.43% over a simulation duration of 1800sec.

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

“…This allows for measurement of the current state of roads in terms of traffic congestion and usage thereby allowing for the use of optimization techniques to improve trip experiences for users and make the transportation system more efficient. The authors in [119][120][121][122][123] work on the minimization of time (wait and travel) in traffic signal control. The aim of such systems is to reduce traffic build up on signal intersections.…”

“…The aim of such systems is to reduce traffic build up on signal intersections. Of these, the work in [119][120][121] use the artificial bee colony and the genetic algorithm respectively for a single objective function of minimizing delay time. An interesting approach for this problem is presented by Li et al [123] who use a multi objective formulation targeting the minimization of the average travel time both overall and individually for all vehiclesl.…”

Recent development of smart traffic lights

“…Level one is used to stop a vehicle from driving in a certain direction, and level two is used to give a warning that recommends not driving in a certain direction. In [21] proposes an artificial bee colony (ABC) algorithm to minimize the average waiting time (AWT) at an intersection based on the dynamic traffic load input. This algorithm is inspired by honeybee swarm behaviour and can optimize the waiting time by 98.43% for a simulation duration of 1800 seconds.…”

Performance Optimization of Adaptive Traffic Lights Using Machine Vision

“…Исследованию вопросов повышения пропускной способности путем использования информационных и коммуникационных технологий посвящен ряд работ [1][2][3][4][5][6][7][8].…”

“…разработали адаптивную систему управления трафиком, которая динамически управляет фазами на перекрестке. Разработанная модель оптимально планирует время зеленого света в соответствии с условиями движения на каждой полосе, чтобы минимизировать среднее время ожидания на перекрестке [2].…”