Real-time traffic signal control with swarm optimization methods

Abstract

Real-time traffic signal control is the control methods that control the traffic signal according to the instant traffic situation. In this paper, it is suggested to optimize the traffic control problem with the swarm-based heuristic optimization algorithms. The proposed methods are tested with the real traffic data obtained from Kilis city in Turkey. The performance is evaluated in real-time via the SUMO traffic simulator. The obtained results are compared with the actual traffic measurement data, and the success of the proposed method is expressed numerically. Finally, it is proved that the particle swarm optimization algorithm and its variance algorithm could be used successfully to optimize the traffic signals control in real traffic. In this study, Social Learning-Particle Swarm Optimization is used as a traffic signal optimizer for the first time in the known literature.