Priority of Dedicated Bus Arterial Control Based on Deep Reinforcement Learning

被引：0

作者：

Shang C.-L. ^{[1
]}

Liu X.-M. ^{[1
]}

Tian Y.-L. ^{[1
]}

Dong L.-X. ^{[1
]}

机构：

[1] Beijing Key Lab of Urban Road Traffic Intelligent Technology, North China University of Technology, Beijing

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2021年 / 21卷 / 03期

基金：

国家重点研发计划;

关键词：

Arterial coordination control; Bus priority control; Dedicated bus; Deep reinforcement learning; Urban traffic;

D O I：

10.16097/j.cnki.1009-6744.2021.03.008

中图分类号：

学科分类号：

摘要：

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. Copyright © 2021 by Science Press.

引用

页码：64 / 70

页数：6

共 9 条

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