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Assessing the Impact of Different Population Density Scenarios on Two-Wheeler Accident Characteristics at Intersections

Author

Listed:
  • Ziwei Wang

    (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)

  • Fang Wang

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

  • Miao Lin

    (China Automobile Technology Research Center Co., Ltd., Tianjin 300300, China)

  • Ning Wu

    (Institute for Traffic Engineering and Management, Ruhr-University, 44801 Bochum, Germany)

Abstract

Examining 1192 intersection car and two-wheeled vehicle collision accidents from the China In-Depth Accident Study (CIDAS) database, this study employs population density heat maps for precise assessment of surrounding population densities at accident sites. The K-Medoid clustering algorithm and silhouette coefficient were used to classify accidents into two distinct groups based on population density. Subsequent application of the random parameter logit model revealed key contributing factors to these accidents in varying population densities. The results show notable differences in factors such as collision direction of two-wheeled vehicles, types of accident conflict, road conditions, and traffic flow, depending on the population density. Based on these conclusions, the research can inform differentiated risk prediction for two-wheeled vehicle accidents at intersections and provide insights for intersection design in various population density scenarios.

Suggested Citation

  • Ziwei Wang & Lin Hu & Fang Wang & Miao Lin & Ning Wu, 2024. "Assessing the Impact of Different Population Density Scenarios on Two-Wheeler Accident Characteristics at Intersections," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1737-:d:1342280
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