Generalized criteria for evaluating hotspot identification methods

Accid Anal Prev. 2020 Sep:145:105684. doi: 10.1016/j.aap.2020.105684. Epub 2020 Aug 13.

Abstract

Hotspot identification (HSID) is one of the most important components in the highway safety management process. Previous research has found that hazardous sites identified with different methods are not consistent. It is therefore necessary to evaluate the performance of various HSID methods. The existing evaluation criteria are limited to two consecutive periods, and do not consider the temporal instability of crashes. In addition, one existing criterion does not precisely evaluate HSID method under given circumstances. This paper proposed three generalized criteria to evaluate the performance of HSID methods: (1) High Crashes Consistency Test (HCCT) is proposed to evaluate HSID methods in terms of their reliabilities of identifying sites with high crash counts; (2) Common Sites Consistency Test (CSCT) is proposed to gauge HSID methods in consistently identifying a set of common sites as hazardous sites; and, (3) Absolute Rank Differences Test (ARDT) is proposed to measure the consistency of HSID methods in measuring the absolute differences in rankings. Further, three commonly used HSID methods are applied to estimate crashes on Texas rural two-lane roadway segments with eight years of crash data. The performance of these three HSID methods were evaluated to validate the proposed criteria. Comparisons between the existing criteria and the generalized criteria revealed that: (1) the generalized criteria are capable of evaluating different HSID methods over multiple periods; and (2) the generalized criteria are enhanced with a consistent result and with less discrepancy in scores of the best identified HSID method.

Keywords: Hotspot identification method; Multiple periods; Network screening; Performance evaluation.

Publication types

  • Validation Study

MeSH terms

  • Accidents, Traffic / prevention & control
  • Accidents, Traffic / statistics & numerical data*
  • Built Environment / statistics & numerical data
  • Humans
  • Models, Statistical
  • Risk Assessment / standards
  • Rural Population
  • Safety Management / methods
  • Texas