Abstract

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.

Highlights

  • Urban arterial roads are important components of the urban traffic system, traffic accidents occurring on them produce a major impact on the entire urban road network and result in huge casualties and large economic losses [1,2,3]

  • The rescue path planning of emergency vehicles will encounter various road conditions; because of the large traffic volume of urban roads, traffic accidents result in road congestion which greatly reduces the capacity of the road upstream of the accident site and makes arrival to the accident site time-consuming

  • Yang et al proposed a path planning method for emergency vehicles, where the road network is divided into a weighted grid and the rescue path is planned by vector grid map method [13]. e green ant method was proposed by Jabbarpour et al to provide path planning for unmanned ground vehicles which leads to low power consumption [14]

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Summary

Research Article

Longhao Yan ,1 Ping Wang ,1 Jingwen Yang ,1 Yu Hu ,1 Yu Han, and Junfeng Yao. Received 7 June 2021; Revised July 2021; Accepted August 2021; Published 1 September 2021. Fast road emergency response can minimize the losses caused by traffic accidents. 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. Is paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. E 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 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; the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. e proposed method is tested on the section of East Youyi Road in Xi’an, Shaanxi Province, China. e 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

Introduction
Methodology
Loss Function
Results and Discussion
Vehicle Queue Lenth Distance to Location Location of Fire Station
Normal traffic density
Reverse main Road
Reverse Side Road
Full Text
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