Revisiting spatial correlation in crash injury severity: a Bayesian generalized ordered probit model with Leroux conditional autoregressive prior

Qiang Zeng, Qianfang Wang, Fangzhou Wang, N. N. Sze

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

To account for the spatial correlation of crashes that are in close proximity, this study proposes a Bayesian spatial generalized ordered probit (SGOP) model with Leroux conditional autoregressive (CAR) prior for crash severity analysis. Proposed model can accommodate the ordinal nature of injury severity and relax the assumption of monotonic effects of explanatory factors. Additionally, strength of spatial correlation is considered. Results indicate that the proposed SGOP model with Leroux CAR prior outperforms the conventional ordered probit model and SGOP model with intrinsic CAR. There is moderate spatial correlation for the crashes. Results indicate that factors including vehicle type, horizontal curvature, vertical grade, precipitation, visibility, traffic composition, day of the week, crash type, and response time of emergency medical service all affect the crash injury severity. Findings of this study can indicate the effective engineering countermeasures that can mitigate the risk of more severe crashes on the freeways.

Original languageEnglish
JournalTransportmetrica A: Transport Science
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • generalized ordered probit model
  • injury severity
  • Leroux conditional autoregressive prior
  • spatial correlation
  • Traffic crash

ASJC Scopus subject areas

  • Transportation
  • Engineering(all)

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