Collaborative Decision-Making Method of Emergency Response for Highway Incidents

Junfeng Yao, Longhao Yan, Zhuohang Xu, Ping Wang () and Xiangmo Zhao
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Junfeng Yao: School of Information Engineering, Chang’an University, Xi’an 710064, China
Longhao Yan: School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
Zhuohang Xu: School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
Ping Wang: School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China
Xiangmo Zhao: School of Information Engineering, Chang’an University, Xi’an 710064, China

Sustainability, 2023, vol. 15, issue 3, 1-23

Abstract: With the continuous increase in highway mileage and vehicles in China, highway accidents are also increasing year by year. However, the on-site disposal procedures of highway accidents are complex, which makes it difficult for the emergency department to fully observe the accident scene, resulting in the lack of sufficient communication and cooperation between multiple emergency departments, making the rescue efficiency low and wasting valuable rescue time, and causing unnecessary injury or loss of life due to the lack of timely assistance. Thus, this paper proposes a multi-agent-based collaborative emergency-decision-making algorithm for traffic accident on-site disposal. Firstly, based on the analysis and abstraction of highway surveillance videos obtained from the Shaanxi Provincial Highway Administration, this paper constructs an emergency disposal model based on Petri net to simulate the emergency on-site disposal procedures. After transforming the emergency disposal model into a Markov game model and applying it to the multi-agent deep deterministic strategy gradient (MADDPG) algorithm proposed in this paper, the multiple agents can optimize the emergency-decision-making and on-site disposal procedures through interactive learning with the environment. Finally, the proposed algorithm is compared with the typical algorithm and the actual processing procedure in the simulation experiment of an actual Shaanxi highway traffic accident. The results show that the proposed emergency-decision-making method could greatly improve collaboration efficiency among emergency departments and effectively reduce emergency response time. This algorithm is not only superior to other decision-making algorithms such as genetic algorithm (EA), evolutionary strategy (ES), and deep Q network (DQN), but also reduces the disposal processes by 28%, 28%, and 42%, respectively, compared with the actual disposal process in three emergency disposal cases. In summary, with the continuous development of information technology and highway management systems, the multi-agent-based collaborative emergency-decision-making algorithm will contribute to the actual emergency response process and emergency disposal in the future, improving rescue efficiency and ensuring the safety of individuals.

Keywords: traffic engineering; emergency decision making; multi-agent deep reinforcement learning; traffic accident; Petri net; Markov game (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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