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Optimizing Kernel Density Estimation Bandwidth for Road Traffic Accident Hazard Identification: A Case Study of the City of London

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Listed:
  • Minxue Zheng

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
    School of the Emergency Management, Jiangsu University, Zhenjiang 212013, China)

  • Xintong Xie

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yutao Jiang

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Qiu Shen

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
    School of the Emergency Management, Jiangsu University, Zhenjiang 212013, China)

  • Xiaolei Geng

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
    School of the Emergency Management, Jiangsu University, Zhenjiang 212013, China)

  • Luyao Zhao

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
    School of the Emergency Management, Jiangsu University, Zhenjiang 212013, China)

  • Feng Jia

    (School of the Environment and Safety Engineering, Jiangsu University, Zhenjiang 212013, China
    School of the Emergency Management, Jiangsu University, Zhenjiang 212013, China)

Abstract

Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS’s automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6969-:d:1456201
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    References listed on IDEAS

    as
    1. 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.
    2. Xu Sun & Hanxiao Hu & Shuo Ma & Kun Lin & Jianyu Wang & Huapu Lu, 2022. "Study on the Impact of Road Traffic Accident Duration Based on Statistical Analysis and Spatial Distribution Characteristics: An Empirical Analysis of Houston," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
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