Title

Exploring A Bayesian Hierarchical Approach For Developing Safety Performance Functions For A Mountainous Freeway

Keywords

Adverse weather; Bayesian model; Curve; Freeway; Safety; Slope

Abstract

While rural freeways generally have lower crash rates, interactions between driver behavior, traffic and geometric characteristics, and adverse weather conditions may increase the crash risk along some freeway sections. This paper examines the safety effects of roadway geometrics on crash occurrence along a freeway section that features mountainous terrain and adverse weather. Starting from preliminary exploration using Poisson models, Bayesian hierarchical models with spatial and random effects were developed to efficiently model the crash frequencies on road segments on the 20-mile freeway section of study. Crash data for 6 years (2000-2005), roadway geometry, traffic characteristics and weather information in addition to the effect of steep slopes and adverse weather of snow and dry seasons, were used in the investigation. Estimation of the model coefficients indicates that roadway geometry is significantly associated with crash risk; segments with steep downgrades were found to drastically increase the crash risk. Moreover, this crash risk could be significantly increased during snow season compared to dry season as a confounding effect between grades and pavement condition. Moreover, sites with higher degree of curvature, wider medians and an increase of the number of lanes appear to be associated with lower crash rate. Finally, a Bayesian ranking technique was implemented to rank the hazard levels of the roadway segments; the results confirmed that segments with steep downgrades are more crash prone along the study section. © 2011 Elsevier Ltd.

Publication Date

7-1-2011

Publication Title

Accident Analysis and Prevention

Volume

43

Issue

4

Number of Pages

1581-1589

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2011.03.021

Socpus ID

79955611692 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79955611692

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