Collaborative Decision-Making Method of Emergency Response for Highway Incidents

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. The on-site disposal procedure of freeway accidents is complicated, and the emergency response time is limited, which makes it difficult for emergency response departments to fully observe the accident scene, leading to the lack of sufficient communication and team cooperation among multiple emergency departments. This paper proposes a multi-agent-based collaborative emergency-decision-making algorithm for traffic accident on-site disposal. Firstly, through analyzing freeway surveillance videos obtained from the Shaanxi Provincial Freeway Administration, this paper constructs an emergency disposal model based on Petri net to simulate the emergency on-site disposal procedures. Then, an emergency-decision-making method based on a multi-agent deep deterministic policy gradient algorithm is proposed to optimize the emergency-decision-making and on-site disposal procedures. Finally, the proposed algorithm is compared with the typical algorithm in a simulation experiment of an actual Shaanxi freeway traffic accident, and the difference between the proposed algorithm and the actual processing procedure is also analyzed. The results show that the proposed emergency-decision-making method could greatly improve team collaboration efficiency among emergency departments and effectively reduce emergency response time. This algorithm is not only superior to other decision-making algorithms, but also reduces the disposal processes by 28%, 28%, and 42%, respectively, compared with the actual disposal process in the three studied cases. It is believed that the continuous development of information technology and freeway management systems will help to improve actual emergency response times and emergency drills in the future.