Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach
Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning m...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/8772688 |
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| author | Longhao Yan Ping Wang Jingwen Yang Yu Hu Yu Han Junfeng Yao |
| author_facet | Longhao Yan Ping Wang Jingwen Yang Yu Hu Yu Han Junfeng Yao |
| author_sort | Longhao Yan |
| collection | DOAJ |
| description | Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi’an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents. |
| format | Article |
| id | doaj-art-2177eac697de41ed9912496921604f5d |
| institution | OA Journals |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2177eac697de41ed9912496921604f5d2025-08-20T02:03:58ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/87726888772688Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning ApproachLonghao Yan0Ping Wang1Jingwen Yang2Yu Hu3Yu Han4Junfeng Yao5School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510275, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaFast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi’an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents.http://dx.doi.org/10.1155/2021/8772688 |
| spellingShingle | Longhao Yan Ping Wang Jingwen Yang Yu Hu Yu Han Junfeng Yao Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach Journal of Advanced Transportation |
| title | Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach |
| title_full | Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach |
| title_fullStr | Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach |
| title_full_unstemmed | Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach |
| title_short | Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach |
| title_sort | refined path planning for emergency rescue vehicles on congested urban arterial roads via reinforcement learning approach |
| url | http://dx.doi.org/10.1155/2021/8772688 |
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