关键词: 城市交通, 公交优先控制, 深度强化学习, 专用道公交, 干线协调控制

Abstract:

Due to the differences in operating characteristics of the social vehicles and dedicated buses and the poor performance of the coordination control, this paper proposes a comprehensive arterial line coordination control method that integrates social vehicle arterial coordination control and public transportation arterial priority control. With the analysis of the dedicated bus non-stop probability of the upstream and downstream, the associated state spaces and the corresponding action decisions were determinded by analysing the difference in the travel time distribution of the social vehicle and dedicated bus. We combined with the influence of signal adjustment strategy on vehicle delay loss and bus priority gains to determine the reward and punishment mechanism. And a deep reinforcement learning framework is proposed to solve the best signal adjustment strategy in real time. Finally, the simulation experiment indicates that the proposed method can reduce the per-capita delay by 38.63% and 27.43%, and the stop times of bus at intersections can decreased by 52.17% compared with the social vehicle arterial coordination, which proves that the method can effectively increase the efficiency of buses and social vehicles.

Key words: urban traffic, bus priority control, deep reinforcement learning, dedicated bus, arterial coordination control