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Bayesian space–time modeling of bicycle and pedestrian crash risk by injury severity levels to explore the long-term spatiotemporal effects

Author

Listed:
  • Wu, Peijie
  • Meng, Xianghai
  • Song, Li

Abstract

Vulnerable road users (VRUs)-related crashes are recognized as an important public safety problem. However, few macro-level studies of VRUs-involved crashes have considered the long-term spatial, temporal, or spatiotemporal effects in the crash risk. This study analyzes the bicycle and pedestrian crash risk in different injury severities by using three multivariate Bayesian space–time models. These models address different spatiotemporal effects to account for possible correlations across injury severities over space and time. Various explanatory variables are used to examine the contributory risk factors, including socio-demographic features, roadway structures, and weather characteristics. Spatio-temporal conditional autoregression with an ANOVA style (ST-CARanova) models outperform other two space–time models in most circumstances. The long-term spatiotemporal effects, such as relatively high temporal autocorrelations, significant spatial heterogeneity, and weak spatiotemporal interactions, are found in this study. The increase of female ratios, young people ratios, unemployment rates, and annual average high temperatures could increase the county-level crash risk of cyclists and pedestrians. The findings provide useful insights for policy makers to improve the safety of cyclists and pedestrians.

Suggested Citation

  • Wu, Peijie & Meng, Xianghai & Song, Li, 2021. "Bayesian space–time modeling of bicycle and pedestrian crash risk by injury severity levels to explore the long-term spatiotemporal effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    • Handle: RePEc:eee:phsmap:v:581:y:2021:i:c:s0378437121004441
    DOI: 10.1016/j.physa.2021.126171
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    References listed on IDEAS

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