Traffic Control Model and Algorithm Based on Decomposition of MDP

In this paper, a new method based on decomposition of Markov Decision Process (MDP) for traffic control at isolated intersection is proposed. The conflicting traffic flows should be grouped into different combinations which can occupy the conflict zone concurrently. Thus, for purpose of traffic delay reduction, the optimal policy of signal sequence and duration among different combinations is studied by minimizing the number of vehicles waiting in the queue. In order to reduce the computation of probabilities in large state transition matrix, the decomposition method proposed classifies states into several parts as rule of traffic signal transition. Each part contains the vehicle states in all traffic flows. This method firstly achieves the full-states calculation in stochastic traffic control system. Moreover, the simulation results indicate that MDP approach is more efficient to improve the performance of traffic control than other comparing methods, such as fixed-time control and actuated control.