Traffic light signal parameters optimization using particle swarm optimization

This paper proposes a traffic light signal parameters optimization using particle swarm optimization (PSO) for real road network called as Ooe Toroku road network. The main aim of this method is to find out the best traffic light signal parameters, which can solve the traffic congestion on the real road network. The traffic light signal parameters that are optimized are offset, cycles, and splits time of each node of the considered road networks. The considered real road network consists of four junctions/nodes having different time signaling models. In this research, the PSO is attached in Aimsun 6.1 simulator via application interface (API) that is provided by Aimsun 6.1 simulator. The PSO algorithm creates n-particles of traffic light signal parameters and sends them to the Aimsun 6.1 simulator to perform the simulation. The output of simulation will be used to perform the particles evaluation and updating. The experimental results show that the proposed method provides better performance than base-line method (multi-element Genetics Algorithms (ME-GA) based optimization method) which can increase the real and base-line percentage of vehicle flow by about 15.76% and 4.13% of that of real and MEGA, respectively. In addition, the PSO is faster to achieve convergence than base-line method for considered network.