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Transit Signal Priority Control with Deep Reinforcement Learning
H. K. Cheng1; K. P. Kou1; K. I. Wong2
2022
Conference Name10th International Conference on Traffic and Logistic Engineering, ICTLE 2022
Source Publication2022 10th International Conference on Traffic and Logistic Engineering, ICTLE 2022
Pages78-82
Conference Date12-14 August 2022
Conference PlaceMacau, China
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Our streets and highways are getting more congested. Transit signal priority (TSP) control which is widely used at signalized intersections has been recognized as a practical strategy to improve the efficiency and reliability of bus operations. Conventional control strategy suffers from the incompetency to adapt to dynamic traffic situations. Recent studies proposed to use deep reinforcement learning (DRL) method to identify an efficient traffic signal control. However, these existing studies in DRL-based traffic signal control methods focus on private vehicles, paying less attention to the difference between transit vehicles and non-transit vehicles. Recently, the concept of 'pressure' from the traffic field has been utilized as the reward function in RL-based traffic signal control. In this study, we adopt the pressure concept and introduce the priority factor (PF) for TSP control. PF increases pressure and that pressure encourages agents to give the way to the bus movements. This is a simple and effective approach granting the buses crossing the signalized intersection. We tested the proposed method in VISSIM with an arterial and a grid network in a dynamic environment. The experiments demonstrate that agents can reduce bus travel time. Moreover, depending on the priority level, the agents can resolve the conflict of different bus routes by different levels of priority.

KeywordDeep Reinforcement Learning Transit Signal Priority Traffic Signal Control
DOI10.1109/ICTLE55577.2022.9902047
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaOperations Research & Management Science ; Transportation
WOS SubjectOperations Research & Management Science ; Transportation Science & Technology
WOS IDWOS:000935123100029
Scopus ID2-s2.0-85141394909
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Citation statistics
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorH. K. Cheng
Affiliation1.Department of Civil and Environmental Engineering, University of Macau, Macao
2.Department of Transportation and LogisticsManagement, National Yang Ming ChiaoTung University, Taiwan
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
H. K. Cheng,K. P. Kou,K. I. Wong. Transit Signal Priority Control with Deep Reinforcement Learning[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2022, 78-82.
APA H. K. Cheng., K. P. Kou., & K. I. Wong (2022). Transit Signal Priority Control with Deep Reinforcement Learning. 2022 10th International Conference on Traffic and Logistic Engineering, ICTLE 2022, 78-82.
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