Abstract
Real-time traffic signal control is an integral part of urban traffic control system. It can control traffic signals online according to variation of traffic flow. In this paper, we propose a new method for the 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 a Particle Swarm Optimization method to calculate optimal traffic signals. A simulation based on real traffic data was carried out to show the effectiveness of the proposed real-time traffic signal control system CAPSOBN using a micro traffic simulator.
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