Real-time traffic signal control for optimization of traffic jam probability

Real-time traffic signal control is an integral part of an urban traffic control system. It can control traffic signals online according to variations of traffic flow. In this paper we propose a new method for a real-time traffic signal control system. The system uses a cellular automaton model and a Bayesian network model to predict probabilistic distributions of standing vehicles, and uses particle swarm optimization to calculate the optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed CAPSOBN real-time traffic signal control system using a micro traffic simulator.