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Data-Driven Analysis of Fatal Urban Traffic Accident Characteristics and Safety Enhancement Research

Author

Listed:
  • Xi Zhang

    (School of Architecture, Harbin Institute of Technology, Shenzhen 518055, China
    Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China)

  • Shouming Qi

    (Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China
    School of Civil Engineering and Environment, Harbin Institute of Technology, Shenzhen 518055, China)

  • Ao Zheng

    (Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China
    School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518000, China)

  • Ye Luo

    (Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518000, China)

  • Siqi Hao

    (School of Port and Shipping Management, Guangzhou Maritime College, Guangzhou 510700, China)

Abstract

The occurrence of fatal traffic accidents often causes serious casualties and property losses, endangering travel safety. This work uses the statistical data of fatal road traffic accidents in Shenzhen from 2018 to 2022 as the basis to determine the characteristic patterns and the main influencing factors of the occurrence of fatal road traffic accidents. The accident description data are also analyzed using the analysis method based on Term Frequency-Inverse Document Frequency (TF-IDF) data mining to obtain the characteristics of accident fields, objects, and types. Furthermore, this work conducts a kernel density analysis combined with spatial autocorrelation to determine the hotspot areas of accident occurrence and analyze their spatial aggregation effects. A principal component analysis is performed to calculate the factors related to the accident subjects. Results showed that weak safety awareness of motorists and irregular driving operations are the main factors for the occurrence of accidents. Finally, targeted safety management strategies are proposed based on the analysis results. In the current data era, the research results of this paper can be used for the prevention and emergency of accidents to formulate corresponding measures, and provide a theoretical basis for decision making.

Suggested Citation

  • Xi Zhang & Shouming Qi & Ao Zheng & Ye Luo & Siqi Hao, 2023. "Data-Driven Analysis of Fatal Urban Traffic Accident Characteristics and Safety Enhancement Research," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3259-:d:1064438
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    References listed on IDEAS

    as
    1. Amin Mallek & Daniel Klosa & Christof Büskens, 2022. "Impact of Data Loss on Multi-Step Forecast of Traffic Flow in Urban Roads Using K-Nearest Neighbors," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    2. Yingliu Yang & Lianghai Jin, 2022. "Visualizing Temporal and Spatial Distribution Characteristic of Traffic Accidents in China," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
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    Cited by:

    1. Yingcui Du & Feng Sun & Fangtong Jiao & Benxing Liu & Xiaoqing Wang & Pengsheng Zhao, 2023. "The Identification of Intersection Entrance Accidents Based on Autoencoder," Sustainability, MDPI, vol. 15(11), pages 1-17, May.

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