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Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach

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  • Huang, Yuan
  • Wang, Xiaoguang
  • Patton, David

Abstract

A better understanding of the relationships between vehicle crashes and the built environment is an important step in improving crash prediction and providing sound policy recommendations that could reduce the occurrence or severity of crashes. Global statistical models are widely used to explore the relationships between vehicle crashes and the built environment, but these models do not incorporate a spatial component and are unable to deal with the issues of spatial autocorrelation and spatial non-stationarity. Our research utilizes a geographically weighted regression (GWR) model to explore the relationships between crashes and the built environment in the context of the Detroit region in Michigan. We find that the relationships between the built environment and crashes are spatially non-stationary: both the strength and the direction of their relationships differ over space. Our study also identifies several built environment variables, such as commercial use percentage, local road mileage percentage, and intersection density, that have relatively stable relationships with crashes. Our research demonstrates the feasibility and value of using spatial models in traffic, transportation, and land use research.

Suggested Citation

  • Huang, Yuan & Wang, Xiaoguang & Patton, David, 2018. "Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach," Journal of Transport Geography, Elsevier, vol. 69(C), pages 221-233.
    • Handle: RePEc:eee:jotrge:v:69:y:2018:i:c:p:221-233
    DOI: 10.1016/j.jtrangeo.2018.04.027
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    References listed on IDEAS

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    3. Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    4. Jonathan Stiles & Yuchen Li & Harvey J Miller, 2022. "How does street space influence crash frequency? An analysis using segmented street view imagery," Environment and Planning B, , vol. 49(9), pages 2467-2483, November.
    5. Wu, Peijie & Chen, Tianyi & Diew Wong, Yiik & Meng, Xianghai & Wang, Xueqin & Liu, Wei, 2023. "Exploring key spatio-temporal features of crash risk hot spots on urban road network: A machine learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    6. Obelheiro, Marta Rodrigues & da Silva, Alan Ricardo & Nodari, Christine Tessele & Cybis, Helena Beatriz Bettella & Lindau, Luis Antonio, 2020. "A new zone system to analyze the spatial relationships between the built environment and traffic safety," Journal of Transport Geography, Elsevier, vol. 84(C).
    7. Choi, Dong-ah & Ewing, Reid, 2021. "Effect of street network design on traffic congestion and traffic safety," Journal of Transport Geography, Elsevier, vol. 96(C).
    8. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    9. Keke Zhang & Shaohua Wang & Chengcheng Song & Sinan Zhang & Xia Liu, 2024. "Spatiotemporal Heterogeneity Analysis of Provincial Road Traffic Accidents and Its Influencing Factors in China," Sustainability, MDPI, vol. 16(17), pages 1-17, August.

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