Analysis of traffic accident characteristics and recovery strategy of urban road network

Abstract Traffic accidents in urban road networks frequently result in significant congestion, further aggravating unsafe conditions. Exploring the congestion propagation from traffic accidents and proposing recovery strategies are crucial for improving urban road network efficiency. We employ the SUMO software to analyze the characteristics of network accidents, including traffic speed, density, occupancy rate, and acceleration. Then, we develop a road network recovery strategy following a traffic accident. The model consists of two parts: (1) A congestion propagation model is constructed, dividing the road network into an “accident core area” and an “accident surrounding area” (2). In the accident core area, traffic signal optimization is applied to improve traffic capacity. Meanwhile, vehicles in the accident surrounding area are guided to avoid the accident core area, thereby reducing traffic pressure. Last, a road network in Zhengzhou city was selected to verify the proposed strategy, with traffic accident points set in the network’s center and edge areas. The results show that the proposed recovery strategy effectively reduces vehicle delay after an accident. When a traffic accident occurs in the edge area of the network, the average vehicle delay is reduced by 6.73%, 16.15%, and 16.84% under low, medium, and high traffic demand, respectively. When a traffic accident occurs in the central area of the network, the average vehicle delay is reduced by 9.91%, 15.56%, and 18.80% under low, medium, and high traffic demand, respectively. Additionally, a sensitivity analysis of the strategy with respect to the traffic accident duration was conducted, demonstrating its good performance.

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Publication Details

Journal
Journal of Engineering and Applied Science
Published
2025-06-13
DOI
https://doi.org/10.1186/s44147-025-00657-1
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
1.01

Funders

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article

Analysis of traffic accident characteristics and recovery strategy of urban road network

Lin Zhang, Yuzhou Duan, Yulong Wang
1 citations
Journal of Engineering and Applied Science
Traffic Prediction and Management Techniques
1.01
article

Analysis of traffic accident characteristics and recovery strategy of urban road network

Lin Zhang, Yuzhou Duan, Yulong Wang
article en
1 citations

Abstract

Abstract Traffic accidents in urban road networks frequently result in significant congestion, further aggravating unsafe conditions. Exploring the congestion propagation from traffic accidents and proposing recovery strategies are crucial for improving urban road network efficiency. We employ the SUMO software to analyze the characteristics of network accidents, including traffic speed, density, occupancy rate, and acceleration. Then, we develop a road network recovery strategy following a traffic accident. The model consists of two parts: (1) A congestion propagation model is constructed, dividing the road network into an “accident core area” and an “accident surrounding area” (2). In the accident core area, traffic signal optimization is applied to improve traffic capacity. Meanwhile, vehicles in the accident surrounding area are guided to avoid the accident core area, thereby reducing traffic pressure. Last, a road network in Zhengzhou city was selected to verify the proposed strategy, with traffic accident points set in the network’s center and edge areas. The results show that the proposed recovery strategy effectively reduces vehicle delay after an accident. When a traffic accident occurs in the edge area of the network, the average vehicle delay is reduced by 6.73%, 16.15%, and 16.84% under low, medium, and high traffic demand, respectively. When a traffic accident occurs in the central area of the network, the average vehicle delay is reduced by 9.91%, 15.56%, and 18.80% under low, medium, and high traffic demand, respectively. Additionally, a sensitivity analysis of the strategy with respect to the traffic accident duration was conducted, demonstrating its good performance.

Journal of Engineering and Applied ScienceVol. 72(1)
Henan University of Technology (CN)
Henan University of Technology, Henan University, Science and Technology Department, Henan Province
Openalex Percentile: Top 24%
Traffic Prediction and Management Techniques
1.01
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