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Predicting and Interpreting Spatial Accidents through MDLSTM

Author

Listed:
  • Tianzheng Xiao

    (Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China)

  • Huapu Lu

    (Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China)

  • Jianyu Wang

    (Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China)

  • Katrina Wang

    (Division of Biosciences, University College London, London WC1E 6BT, UK)

Abstract

Predicting and interpreting the spatial location and causes of traffic accidents is one of the current hot topics in traffic safety. This research purposed a multi-dimensional long-short term memory neural network model (MDLSTM) to fit the non-linear relationships between traffic accident characteristics and land use properties, which are further interpreted to form local and general rules. More variables are taken into account as the input land use properties and the output traffic accident characteristics. Five types of traffic accident characteristics are simultaneously predicted with higher accuracy, and three levels of interpretation, including the hidden factor-traffic potential, the potential-determine factors, which varies between grid cells, and the general rules across the whole study area are analyzed. Based on the model, some interesting insights were revealed including the division line in the potential traffic accidents in Shenyang (China). It is also purposed that the relationship between land use and accidents differ from previous researches in the neighboring and regional aspects. Neighboring grids have strong spatial connections so that the relationship of accidents in a continuous area is relatively similar. In a larger region, the spatial location is found to have a great influence on the traffic accident and has a strong directionality.

Suggested Citation

  • Tianzheng Xiao & Huapu Lu & Jianyu Wang & Katrina Wang, 2021. "Predicting and Interpreting Spatial Accidents through MDLSTM," IJERPH, MDPI, vol. 18(4), pages 1-18, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:4:p:1430-:d:492745
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    References listed on IDEAS

    as
    1. Dereli, Mehmet Ali & Erdogan, Saffet, 2017. "A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 106-117.
    2. 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. Yuquan Zhou & Yingzhi Wang & Feng Zhang & Hongye Zhou & Keran Sun & Yuhan Yu, 2023. "GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network," IJERPH, MDPI, vol. 20(4), pages 1-18, February.

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