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.

No abstract

Nowadays traffic 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 traffic control algorithms can effectively reduce traffic congestion. However, those works 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 back-pressure 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 38% when compared with a state-of-the-art algorithm under tested scenarios.

No abstract