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.

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.

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Vehicular social networks are emerging hybrid networks that combine traditional vehicular networks and social networks, with two key types of nodes, that is, vehicles and drivers. Since vehicle behaviors are controlled or influenced by drivers, the trustworthiness of a vehicle node is essentially determined by its own communication behaviors and its driver’s social characteristics. Therefore, human factors should be considered in securing the communication in vehicular social networks. In this article, we propose a hybrid trust model that considers both communication trust and social trust. Within the proposed scheme, we first construct a communication trust model to quantify the trust value based on the interactions between vehicle nodes, and then develop a social trust model to measure the social trust based on the social characteristics of vehicle drivers. Based on these two trust models, we compute the combined trust assessment of a vehicle node in vehicular social networks. Extensive simulations show that the proposed hybrid trust model improves the accuracy in evaluating the trustworthiness of vehicle nodes and the efficiency of communication in vehicular social networks.

Accurate traffic flow forecasting is a critical component in intelligent transportation systems. However, most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes on the prediction results. This study applies a hybrid deep learning model based on multi feature fusion to predict traffic flow considering weather conditions. A comparison with other representative models validates that the proposed spatial‐temporal fusion graph convolutional network (STFGCN) can achieve better performance.