Forward search algorithm based on dynamic programming for real-time adaptive traffic signal control

Abstract

The scheduling of traffic signal at intersections is involved in an application of artificial intelligence system. This study presents a new forward search algorithm based on dynamic programming (FSDP) under a decision tree, and explores an efficient solution for real-time adaptive traffic signal control policy. Traffic signal control with cases of fixed phase sequence and variable phase sequence are both considered in the algorithm. Owing to the properties of forward research dynamic programming and the process optimisation of repeated or invalid traffic states the authors proposed, FSDP algorithm reduces the number of states and saves much computation time. Consequently, FSDP is certain to be an on-line algorithm through its application to a complicated traffic control problem. Moreover, the labelled position method is firstly proposed in the author's study to search the optimal policy after reaching the goal state. For practical operations, this new algorithm is extended by adding the rolling horizon approach, and some derived methods are compared with the optimal fixed-time control and adaptive control on the evaluation of traffic delay. Experimental results obtained by the simulations of symmetrical and asymmetrical traffic flow scenarios show that the FSDP method can perform quite well with high efficiency and good qualities in traffic control.