Effects Of Pavement Surface Conditions On Traffic Crash Severity

Keywords

Bayesian modeling; GIS analysis; Ordered logistic model; Pavement management; Random forest; Severity analysis; Traffic safety; Variable importance

Abstract

Improving road safety through proper pavement maintenance is one of the goals of pavement management. Many studies have found that pavement conditions significantly influence traffic safety. Although several studies have explored the relationship between pavement conditions and crash occurrence, the effect of poor pavement conditions on crash severity levels has not been investigated, especially by using a discrete model that can handle ordered data. This paper focuses on the development of the relationship between poor pavement conditions and crash severity levels using a series of Bayesian ordered logistic models for low/medium/high speed roads and single/multiple collision cases. The Bayesian ordered logistic regression models indicated that the poor pavement condition decreases the severity of singlevehicle collisions on low-speed roads whereas it increases their severity on high-speed roads. On the other hand, the poor pavement condition increases the severity of multiple-vehicle crashes on all roads. Findings of this study can assist transportation agencies at the federal, state, and local levels to select appropriate pavement maintenance and rehabilitation strategies to reduce traffic crash severity levels.

Publication Date

10-1-2015

Publication Title

Journal of Transportation Engineering

Volume

141

Issue

10

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1061/(ASCE)TE.1943-5436.0000785

Socpus ID

84939834576 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84939834576

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