Citation

Wang B, Hallmark SL, Savolainen P, Dong J. J. Saf. Res. 2017; 63: 163-169.

Affiliation

Department of Civil, Construction & Environmental Engineering, Iowa State University, Ames, IA 50011, United States. Electronic address: jingdong@iastate.edu.

Copyright

(Copyright © 2017, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2017.10.001

PMID

29203015

Abstract

INTRODUCTION: Prior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.

METHODS: The second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.

RESULTS: Logistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.

CONCLUSIONS: This paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data. PRACTICAL APPLICATIONS: This paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.

Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

Language: en

Keywords

Horizontal curve; Naturalistic driving study; Rural two-lane highway; SHRP 2; Traffic safety