Residential College | false |
Status | 已發表Published |
Transit Signal Priority Control with Deep Reinforcement Learning | |
H. K. Cheng1; K. P. Kou1; K. I. Wong2 | |
2022 | |
Conference Name | 10th International Conference on Traffic and Logistic Engineering, ICTLE 2022 |
Source Publication | 2022 10th International Conference on Traffic and Logistic Engineering, ICTLE 2022 |
Pages | 78-82 |
Conference Date | 12-14 August 2022 |
Conference Place | Macau, China |
Publisher | IEEE, 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. |
Keyword | Deep Reinforcement Learning Transit Signal Priority Traffic Signal Control |
DOI | 10.1109/ICTLE55577.2022.9902047 |
URL | View the original |
Indexed By | CPCI-S |
Language | 英語English |
WOS Research Area | Operations Research & Management Science ; Transportation |
WOS Subject | Operations Research & Management Science ; Transportation Science & Technology |
WOS ID | WOS:000935123100029 |
Scopus ID | 2-s2.0-85141394909 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | Faculty of Science and Technology DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING |
Corresponding Author | H. K. Cheng |
Affiliation | 1.Department of Civil and Environmental Engineering, University of Macau, Macao 2.Department of Transportation and LogisticsManagement, National Yang Ming ChiaoTung University, Taiwan |
First Author Affilication | University of Macau |
Corresponding Author Affilication | University 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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