Despite the numerous breakthroughs in crash analytics, there remains a lack of consensus among safety practitioners as to the optimal method for locating high crash locations. Two critical components in the traffic safety analysis process not agreed upon are 1) how the crash distance to a target location is included in the analysis and 2) how crashes are weighted based on crash-related characteristics. For example, the commonly used buffering technique to determine which crashes are associated with a specific target road segment does not associate crashes that are closer to a target road segment with any additional weight, even though it is likely to be more greatly associated with the characteristics of the target location. Additionally, the commonly used equivalent property damage only (EPDO) crash weight method has been found to weigh fatal crashes significantly more than serious injury crashes, even if the difference between the two outcomes was a single factor. This study proposes more robust crash weighting techniques for use in high-risk location identification using an application of a novel horizontal curve dataset. Specifically, a heteroscedastic censored regression approach was used to investigate the impact of different crash proximity weighting techniques and crash severity weighting methods on model outcomes. The results demonstrate that the use of a linear distance weighting factor used in conjunction with the buffering technique as well as a less precise EPDO weighting factor method results in more robust safety analysis outcomes. The improved results have the potential to improve hot spot identification and resource allocation at both the federal and regional levels by employing models that more accurately link specific crash segments with contributing crash characteristics.
Keywords: Crash proximity; Crash weighting; Equivalent property damage only; Horizontal curve; Resource allocation; Safety.
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