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Spatial analysis of geographical disparities in pedestrian safety

Author

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  • Liu, Jinli
  • Das, Subasish
  • Zhan, F. Benjamin
  • Khan, Md Nasim

Abstract

Investigating pedestrian safety disparities across sociodemographic groups is essential for enhancing traffic safety. This study examines the impact of sociodemographic and built environment characteristics on pedestrian crashes. It introduces a comprehensive macro spatial analysis framework that includes a global regression model, spatial autoregressive models, and a local spatial regression model. Three measures of pedestrian injury are analyzed. The findings reveal that a higher percentage of the high-income population significantly correlates with lower rates of pedestrian injuries across all three measures. Conversely, a higher percentage of the low-income population shows a significant positive correlation with the proportion of crashes involving the Black population, and with the proportion of severe pedestrian crashes involving the Black population. Pedestrian-oriented network density is negatively associated with fatal or severely injurious crashes involving the Black population. These results emphasize the need to account for spatial variations and equity when addressing pedestrian safety disparities.

Suggested Citation

  • Liu, Jinli & Das, Subasish & Zhan, F. Benjamin & Khan, Md Nasim, 2024. "Spatial analysis of geographical disparities in pedestrian safety," Transport Policy, Elsevier, vol. 156(C), pages 164-181.
    • Handle: RePEc:eee:trapol:v:156:y:2024:i:c:p:164-181
    DOI: 10.1016/j.tranpol.2024.06.018
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    References listed on IDEAS

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    5. Jin, Scarlett T. & Wang, Lei & Sui, Daniel, 2023. "How the built environment affects E-scooter sharing link flows: A machine learning approach," Journal of Transport Geography, Elsevier, vol. 112(C).
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