Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning

Abstract

Nowadays traffic congestion has increasingly been a significant problem, which results in longer travel time and aggravates air pollution. Available work showed that back-pressure based traffic control algorithms can effectively reduce traffic congestion. However, those work control traffic based on either inaccurate traffic information or local traffic information, which causes inefficient traffic scheduling. In this paper, we propose an adaptive traffic control algorithm based on backpressure and Q-learning, which can efficiently reduce congestion. Our algorithm controls traffic based on accurate real-time traffic information and global traffic information learned by Q-learning. As verified by simulation, our algorithm significantly decreases average vehicle traveling time from 17% to 37% when compared with state-of-the-art algorithm under tested scenarios.

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

“…The proposed algorithms were previously presented in Refs. [13] and [14], respectively. This paper consists of the explanation of the proposed algorithms and experiment results with additional results.…”

“…We have proposed adaptive traffic signal control methods based on back-pressure with global traffic information [13], [14]. Reference [13] describes an adaptive traffic signal control method using real-time traffic information with global traffic information in a road network.…”

“…Therefore, Ref. [14] employs the approach in which control agents are placed to static equipment on each junction and exchange information only with their neighboring agents. Q-learning is used to estimate global congestion information from limited information.…”