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Addressing spatial heterogeneity of injury severity using Bayesian multilevel ordered probit model

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  • Xu, Xuecai
  • Huang, Dong
  • Guo, Fengjun

Abstract

The objective of this study is to investigate the impact of influencing factors on crash injury severity at signalized intersections by employing a multilevel ordered probit model, which captures the spatial heterogeneity. The estimation was performed with Bayesian approach via Markov Chain Monte Carlo (MCMC) sampling. The crash data of 262 signalized intersections were used for two years from Hong Kong. The findings indicated that the multilevel ordered probit model can effectively address the spatial heterogeneity and provide better model fit than conventional ordered probit model. The results showed that at intersection-level number of conflicts and reciprocal of the turning radius are statistically significant for the crash injury severity while at arterial-level lane width, proportion of commercial vehicle and the presence of tram stops increases the injury severity. More importantly, about 38.3% of the spatial heterogeneity in crash injury severity is attributed to the arterial-level variables. Our findings can help designers and management departments develop a better understanding on intersection and arterial design and operation.

Suggested Citation

  • Xu, Xuecai & Huang, Dong & Guo, Fengjun, 2020. "Addressing spatial heterogeneity of injury severity using Bayesian multilevel ordered probit model," Research in Transportation Economics, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:retrec:v:80:y:2020:i:c:s0739885919302604
    DOI: 10.1016/j.retrec.2019.100748
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    References listed on IDEAS

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    More about this item

    Keywords

    Multilevel binary probit model; Bayesian approach; Spatial heterogeneity; Injury severity; Signalized intersection;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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