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Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

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  • Qing Ye

    (National Engineering and Research Center for Mountainous Highways, Chongqing 400067, China
    Research and Development Center of Transport Industry of Self-Driving Technology, China Merchants Chongqing Communications Technology Research and Design Institute Co., Ltd., Chongqing 400067, China
    School of Big Data & Software Engineering, Chongqing University, Chongqing 400030, China
    These authors contributed equally to this study.)

  • Yi Li

    (Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China
    These authors contributed equally to this study.)

  • Wenzhe Shen

    (Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China)

  • Zhaoze Xuan

    (Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China)

Abstract

This study focuses on identifying accident-prone areas and analyzing the factors contributing to the distribution of traffic accidents near highway ramps. A combined method of kernel density estimation, spatial autocorrelation analysis, and multivariate logistic regression analysis helped to identify accident hotspots. Through data collection and analysis, the clustering characteristics of traffic accidents in the diversion and merging areas were identified. Four levels of accident-prone areas were divided according to the accident rates. The factors influencing the spatial distribution of accidents were analyzed. The results showed that traffic accidents in the diversion area were concentrated near the exit, but the accidents in merging areas had a wider range of distribution. The analysis of this phenomenon was conducted using the multinomial logit model results. The important factors of different accident-prone areas were clarified. The temperature, the accident lane, weather conditions, and the time of day had significant impacts on the spatial distribution of traffic accidents. The study’s findings provide an important decision-making basis for highway accident prevention management.

Suggested Citation

  • Qing Ye & Yi Li & Wenzhe Shen & Zhaoze Xuan, 2023. "Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7942-:d:1145554
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    References listed on IDEAS

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    1. Maosheng Li & Hui Xie & Panpan Shu, 2021. "Study on the Impact of Traffic Accidents in Key Areas of Rural Roads," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    2. Nuntaporn Klinjun & Matthew Kelly & Chanita Praditsathaporn & Rewwadee Petsirasan, 2021. "Identification of Factors Affecting Road Traffic Injuries Incidence and Severity in Southern Thailand Based on Accident Investigation Reports," Sustainability, MDPI, vol. 13(22), pages 1-17, November.
    3. Yuhuan Zhang & Huapu Lu & Wencong Qu, 2020. "Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors," IJERPH, MDPI, vol. 17(2), pages 1-17, January.
    4. Maowei Chen & Lele Zhou & Sangho Choo & Hyangsook Lee, 2022. "Analysis of Risk Factors Affecting Urban Truck Traffic Accident Severity in Korea," Sustainability, MDPI, vol. 14(5), pages 1-14, March.
    5. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
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    Cited by:

    1. Minxue Zheng & Xintong Xie & Yutao Jiang & Qiu Shen & Xiaolei Geng & Luyao Zhao & Feng Jia, 2024. "Optimizing Kernel Density Estimation Bandwidth for Road Traffic Accident Hazard Identification: A Case Study of the City of London," Sustainability, MDPI, vol. 16(16), pages 1-17, August.

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