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Collaborative Decision-Making Method of Emergency Response for Highway Incidents

Author

Listed:
  • Junfeng Yao

    (School of Information Engineering, Chang’an University, Xi’an 710064, China
    China Communications Information & Technology Group Co., Ltd., Beijing 100088, China
    These authors contributed equally to this work.)

  • Longhao Yan

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
    These authors contributed equally to this work.)

  • 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
    School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China)

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.

Suggested Citation

  • Junfeng Yao & Longhao Yan & Zhuohang Xu & Ping Wang & Xiangmo Zhao, 2023. "Collaborative Decision-Making Method of Emergency Response for Highway Incidents," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2099-:d:1044103
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    References listed on IDEAS

    as
    1. Huacai Xian & Yu Wang & Yujia Hou & Shunzhong Dong & Junying Kou & Huili Zeng, 2022. "Research on Influencing Factors of Urban Road Traffic Casualties through Support Vector Machine," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    2. Xu Sun & Hanxiao Hu & Shuo Ma & Kun Lin & Jianyu Wang & Huapu Lu, 2022. "Study on the Impact of Road Traffic Accident Duration Based on Statistical Analysis and Spatial Distribution Characteristics: An Empirical Analysis of Houston," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    3. Yingliu Yang & Lianghai Jin, 2022. "Visualizing Temporal and Spatial Distribution Characteristic of Traffic Accidents in China," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
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