Crash proximity and equivalent property damage calculation techniques: An investigation using a novel horizontal curve dataset

Crash modification factors for high friction surface treatment on horizontal curves of two-lane highways: A combined propensity scores matching and empirical Bayes before-after approach

Horizontal curves are locations that, as a result of the changing alignment, may be a contributing factor in roadway departure crashes. One low-cost countermeasure to mitigate crashes at these locations is the installation of the high friction surface treatment (HFST), which increases roadway friction and is intended to help keep drivers on the roadway when traversing a horizontal curve. This treatment has been implemented at numerous curves in Pennsylvania, but the overall safety effectiveness is not known. The purpose of this study is to estimate a suite of Crash Modification Factors (CMFs) for HFST applied to curve sections of undivided two-lane roadways. A novel combination of the empirical Bayes observational before-after study design and propensity score matching was used to estimate CMFs for multiple crash types, crash severities, and roadway settings (urban and rural). Propensity score matching was implemented to identify the most appropriate reference group to use within the empirical Bayes methodology. The results indicate that the installation of HFST is associated with a statistically significant decrease in all crash types and severities considered.

Safety performance analysis of horizontal curves in urban areas

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.