An Adaptive Traffic Signal Control Scheme Based on Back-pressure with Global Information

: Nowadays tra ffi c congestion has increasingly been a significant problem, which results in a longer travel time and aggravates air pollution. Available works showed that back-pressure based tra ffi c control algorithms can ef-fectively reduce tra ffi c congestion. However, those works control tra ffi c based on either inaccurate tra ffi c information or local tra ffi c information, which causes ine ffi cient tra ffi c scheduling. In this paper, we propose an adaptive tra ffi c control algorithm based on back-pressure and Q-learning, which can e ffi ciently reduce congestion. Our algorithm controls tra ffi c based on accurate real-time tra ffi c information and global tra ffi c information learned by Q-learning. As verified by simulation, our algorithm significantly decreases average vehicle traveling time from 17% to 38% when compared with a state-of-the-art algorithm under tested scenarios.