Title

Using Hierarchical Bayesian Binary Probit Models To Analyze Crash Injury Severity On High Speed Facilities With Real-Time Traffic Data

Keywords

Bayesian inference; Binary probit model; Crash injury severity; Hierarchical probit model; Random effects

Abstract

Severe crashes are causing serious social and economic loss, and because of this, reducing crash injury severity has become one of the key objectives of the high speed facilities' (freeway and expressway) management. Traditional crash injury severity analysis utilized data mainly from crash reports concerning the crash occurrence information, drivers' characteristics and roadway geometric related variables. In this study, real-time traffic and weather data were introduced to analyze the crash injury severity. The space mean speeds captured by the Automatic Vehicle Identification (AVI) system on the two roadways were used as explanatory variables in this study; and data from a mountainous freeway (I-70 in Colorado) and an urban expressway (State Road 408 in Orlando) have been used to identify the analysis result's consistence. Binary probit (BP) models were estimated to classify the non-severe (property damage only) crashes and severe (injury and fatality) crashes. Firstly, Bayesian BP models' results were compared to the results from Maximum Likelihood Estimation BP models and it was concluded that Bayesian inference was superior with more significant variables. Then different levels of hierarchical Bayesian BP models were developed with random effects accounting for the unobserved heterogeneity at segment level and crash individual level, respectively. Modeling results from both studied locations demonstrate that large variations of speed prior to the crash occurrence would increase the likelihood of severe crash occurrence. Moreover, with considering unobserved heterogeneity in the Bayesian BP models, the model goodness-of-fit has improved substantially. Finally, possible future applications of the model results and the hierarchical Bayesian probit models were discussed. © 2013 Elsevier Ltd. All rights reserved.

Publication Date

1-1-2014

Publication Title

Accident Analysis and Prevention

Volume

62

Number of Pages

161-167

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2013.08.009

Socpus ID

84887001951 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84887001951

This document is currently not available here.

Share

COinS