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Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

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  • Shaohua Wang
  • Yanyan Chen
  • Jianling Huang
  • Ning Chen
  • Yao Lu

Abstract

This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

Suggested Citation

  • Shaohua Wang & Yanyan Chen & Jianling Huang & Ning Chen & Yao Lu, 2019. "Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-10, May.
  • Handle: RePEc:hin:jnlmpe:5306247
    DOI: 10.1155/2019/5306247
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    Cited by:

    1. Soltani, Ali & Roohani Qadikolaei, Mohsen, 2024. "Space-time analysis of accident frequency and the role of built environment in mitigation," Transport Policy, Elsevier, vol. 150(C), pages 189-205.
    2. Tibor Sipos & Anteneh Afework Mekonnen & Zsombor Szabó, 2021. "Spatial Econometric Analysis of Road Traffic Crashes," Sustainability, MDPI, vol. 13(5), pages 1-16, February.
    3. Haiyan Lei & Suiping Zeng & Aihemaiti Namaiti & Jian Zeng, 2023. "The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies," Land, MDPI, vol. 12(4), pages 1-20, March.
    4. Shanshan Guo & Zhiqiang Han & Jun Wei & Shenggang Guo & Liang Ma, 2022. "A Novel DC-AC Fast Charging Technology for Lithium-Ion Power Battery at Low-Temperatures," Sustainability, MDPI, vol. 14(11), pages 1-10, May.
    5. Guo, Shanshan & Yang, Ruixin & Shen, Weixiang & Liu, Yongsheng & Guo, Shenggang, 2022. "DC-AC hybrid rapid heating method for lithium-ion batteries at high state of charge operated from low temperatures," Energy, Elsevier, vol. 238(PB).

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