Citation

Bejleri I, Xu X, Silva KR, Srinivasan S. Accid. Anal. Prev. 2023; 195: e107402.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.aap.2023.107402

PMID

38070355

Abstract

While numerous studies have examined horizontal curve risk factors in rural areas, there is only one study for urban areas. Moreover, previous studies have used limited datasets, which tend to generate an intrinsic bias on results either by the sample size or due to a lack of understanding of all the risk factors associated with curve safety. This study aims to narrow this knowledge gap in three aspects: it focuses on urban areas; it uses a large novel GIS dataset of about 25,000 urban curves; and it expands the traditional curve risk factor pool by examining the spatial relationship of curves to adjacent curves and intersections. Using this curve dataset and six years of statewide fatal and injury crash data in the state of Florida, the study develops customized safety performance functions (SPFs) for urban curves based on different spatial relationships of curves to intersections. The results confirm that the traditional risk factors for rural curves, such as traffic volume, curve radius and length, speed limit, functional classification, and the number of lanes, also apply to curves in urban areas. However, the new finding is that curve safety in urban areas is affected by the proximity of curves to adjacent curves and intersections. The curves with intersections and isolated curves (with no adjacent nearby curves) are at high risk. There are also risk factor differences between single and dual-centerline roads. We also observed differences between the travel directions on divided roadway curves, but these differences will require more research.

Language: en

Keywords

Negative binomial regression; GIS; Horizontal curve; Roadway safety; Safety performance function