The Influence of Underreported Crashes on Hotspot Identification

Hotspot identification (HSID) plays a significant role in improving the safety of transportation networks. Numerous HSID methods have been proposed, developed, and evaluated in the literature. The vast majority of HSID methods reported and evaluated in the literature assume that crash data are complete, reliable, and accurate. Crash underreporting, however, has long been recognized as a threat to the accuracy and completeness of historical traffic crash records. As a natural continuation of prior studies, this paper evaluates the influence that underreported crashes exert on HSID methods. To conduct the evaluation, five groups of data gathered from Arizona Department of Transportation (ADOT; USA) over the course of three years are adjusted to account for 15 different assumed levels of underreporting. Three identification methods are evaluated: simple ranking (SR), empirical Bayes (EB), and full Bayes (FB). Various threshold levels for establishing hotspots are explored. Finally, two evaluation criteria are compared across hotspot identification (HSID) methods. The results illustrate that the identification bias—the ability to correctly identify at risk sites— underreporting is influenced by the degree of underreporting. Comparatively speaking, crash underreporting has the largest influence on the FB method and the least influence on the SR method. Additionally, the impact is positively related to the percentage of the underreported property damage only (PDO) crashes and inversely related to the percentage of the underreported injury crashes. This finding is significant because it reveals that, despite PDO crashes being least severe and costly, they have the most significant influence on the accuracy of HSID.

Language

  • English

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Filing Info

  • Accession Number: 01345166
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: Jul 7 2011 9:56AM