Are collision and crossing course surrogate safety indicators transferable? A probability based approach using extreme value theory

Accid Anal Prev. 2020 Aug:143:105517. doi: 10.1016/j.aap.2020.105517. Epub 2020 May 29.

Abstract

In order to overcome the shortcomings of crash data a number of surrogate measures of safety have been developed and proposed by various researchers. One of the most widely used temporal indicators is time-to-collision (TTC) which requires the road users to be on a collision course. Road users that are strictly speaking not on a collision course actually might behave and take evasive actions as if they were, thus indicating that such near-miss situations might also be relevant for safety analysis. Taking that into account, a more flexible indicator T2, which does not require the two vehicles to be on a collision course, describes the expected time for the second road user to arrive at the conflict point. Recently extreme value theory (EVT) offering two approaches, block maxima (BM) and Peak over Threshold (POT), has been applied in combination with surrogate indicators to estimate crash probabilities. Most of this research has focused on testing BM and POT as well as validating various surrogate safety indicators by comparing model estimates to actual crash frequencies. The comparison of collision course indicators with indicators including crossing course interactions and their performance using EVT has not been investigated yet. In this study we are seeking answers to under what conditions these indicators perform better and whether they are transferable. Using data gathered at a signalized intersection focusing on left-turning and straight moving vehicle interactions our analysis concluded that the two indicators are transferable with stricter threshold values for T2 and that POT gives more reasonable results.

Keywords: Collision and crossing course; Crash probability; Extreme value theory; Surrogate measures of safety.

MeSH terms

  • Accidents, Traffic / prevention & control*
  • Automobile Driving*
  • Humans
  • Probability
  • Risk Management