Adaptive Traffic Signal Control for Multi-intersection Based on Microscopic Model

In this paper, we mainly propose an online learning method for adaptive traffic signal control in a multi-intersection system. The method uses approximate dynamic programming (ADP) to achieve a near-optimal solution of the signal optimization in a distributed network, which is modeled in a microscopic way. The traffic network loading model and traffic signal control model are presented to serve as the basis of discrete-time control environment. The learning process of linear function approximation in ADP approach adopts the tunable parameters of the traffic states, including the vehicle queue length and the signal indication. ADP overcomes the computational complexity, which usually appears in large scale problems solved by exact algorithms, such as dynamic programming. Moreover, the proposed adaptive phase sequence (APS) mode improves the performance by comparing with other control methods. The results in simulation show that our method performs quite well for adaptive traffic signal control problem.