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Regional public transportation safety risk grading assessment under time dimension: A case study of Chinese mainland

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  • Zhang, Jietao
  • Tu, Yan
  • Liu, Jun
  • Liu, Liyi
  • Li, Zongmin

Abstract

Traffic risk hinders the development of human society. Due to the influence of weather and climate, the traffic risks vary in different time periods. This paper innovatively reconstructs the criterion system for evaluating regional traffic safety risk from a time series perspective. On the basis of the novel criterion system, this paper proposes a monthly dimension regional public traffic safety risk grading model with Analytic Hierarchy Process Sort II (AHPSort II) and Criteria Importance Though Intercriteria Correlation (CRITIC) (i.e., AHPSort II-CRITIC method), in which, AHPSort II is used to obtain the traffic safety risk levels of different regions, and the CRITIC is used to determine the weight vector of the criteria system. The proposed method is applied to evaluate the regional traffic safety risk levels of 31 provinces, municipalities, and autonomous regions in the Chinese mainland and some cities in Hubei province in the whole year of 2019. The results of this paper show that the traffic safety risk levels are high in June, July, and August, and the performances of other months are relatively more optimistic. Finally, this paper puts forward some transport policy implications, which provide a suitable basis for policy formulation to reduce regional traffic safety risk.

Suggested Citation

  • Zhang, Jietao & Tu, Yan & Liu, Jun & Liu, Liyi & Li, Zongmin, 2022. "Regional public transportation safety risk grading assessment under time dimension: A case study of Chinese mainland," Transport Policy, Elsevier, vol. 126(C), pages 343-354.
  • Handle: RePEc:eee:trapol:v:126:y:2022:i:c:p:343-354
    DOI: 10.1016/j.tranpol.2022.08.003
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    References listed on IDEAS

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    Cited by:

    1. Zheng, Qikang & Sharmeen, Fariya & Xu, Chengcheng & Liu, Pan, 2024. "Assessing regional road traffic safety in Sweden through dynamic panel data analysis: Influence of the planned innovative policies and the unplanned COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

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