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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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    1. Marcucci, Edoardo & Gatta, Valerio, 2012. "Dissecting preference heterogeneity in consumer stated choices," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 331-339.
    2. Pavlyuk, Dmitry, 2016. "Implication of spatial heterogeneity for airports' efficiency estimation," Research in Transportation Economics, Elsevier, vol. 56(C), pages 15-24.
    3. Velmurugan, S. & Padma, S. & Madhu, E. & Anuradha, S. & Gangopadhyay, S., 2013. "A study of factors influencing the severity of road crashes involving drunk drivers and non drunk drivers," Research in Transportation Economics, Elsevier, vol. 38(1), pages 78-83.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    6. Abay, Kibrom A., 2013. "Examining pedestrian-injury severity using alternative disaggregate models," Research in Transportation Economics, Elsevier, vol. 43(1), pages 123-136.
    Full references (including those not matched with items 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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