Optimization of Traffic Signal Timing on Road Network Using Cellular Automata and Fuzzy Inference System

The large number of vehicles in big cities has become a serious problem in adjusting the timing of traffic signal at intersections of road networks. Traffic light control is an important factor in road traffic system. In this paper, optimization of traffic signal timing at signalized intersections on road network is presented. The movement of vehicles on road network is modeled by cellular automata while the fuzzy inference system is used to obtain optimal traffic signal timing. The performance of fuzzy inference system is determined by the delay time average at signalized intersections. Delay time is addition time required by a vehicle to pass through signalized intersection compared to road without intersection. Cellular automata and fuzzy inference system are running together through computer simulation program. The optimal signal timing from the simulation is compared to fixed time or static scheme which is obtained from historical data of six signalized intersections in Bandung City, Indonesia. The numerical results show that traffic signal duration obtained from fuzzy inference system is more efficient than the existing historical data of observation and it can reduce the level of congestion. The simulation results also show that the traffic signal timing can adjust with the number of incoming and outgoing of vehicles at the intersections on road network. The results can be suggested to the local government to improve the traffic signal system.