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Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment

Author

Listed:
  • Ruizhe Yan

    (School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Lin Hu

    (School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Juanjuan Li

    (School of Design, Changsha University of Science and Technology, Changsha 410114, China)

  • Nanting Lin

    (School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China)

Abstract

Examining the impacts of accident characteristics and differentiated built environment factors on accident severity at inherent accident hotspots within cities can help managers to adjust traffic control measures through urban planning and design, thereby reducing accident casualties. In this study, time series clustering was used to identify traffic accident hotspots in Changsha City. Based on the hotspot identification results, Kruskal–Wallis tests were used to select differentiated built environment factors among different accident areas within the city. A severity analysis model for road crashes in Changsha’s hotspots, taking into account the built environment, was constructed using a Light gradient boosting machine (LightGBM). In addition, Shapley additive explanations (SHAP) were used to reveal the influences of accident characteristics and built environment factors on accident severity. The results showed that different accident characteristics and built environment factors affect accident severity in different urban accident areas. Factors such as type of accident, visibility, period of time, land use mixing degree, population density, density of commercial places, and density of industrial places showed varying degrees of importance in influencing accident severity, while the overall impact trends remained consistent. On the other hand, transportation accessibility, road network density, landform, and accident location showed significant differences in their impacts on accident severity between different accident areas within the city.

Suggested Citation

  • Ruizhe Yan & Lin Hu & Juanjuan Li & Nanting Lin, 2024. "Accident Severity Analysis of Traffic Accident Hot Spot Areas in Changsha City Considering Built Environment," Sustainability, MDPI, vol. 16(7), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:3054-:d:1371016
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    References listed on IDEAS

    as
    1. Jianyu Wang & Lanxin Ji & Shuo Ma & Xu Sun & Mingxin Wang, 2023. "Analysis of Factors Influencing the Severity of Vehicle-to-Vehicle Accidents Considering the Built Environment: An Interpretable Machine Learning Model," Sustainability, MDPI, vol. 15(17), pages 1-17, August.
    2. Junwei Zeng & Yongsheng Qian & Fan Yin & Leipeng Zhu & Dejie Xu, 2022. "A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate," Computational and Mathematical Organization Theory, Springer, vol. 28(2), pages 178-192, June.
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