Before-after safety analysis using extreme value theory: A case of left-turn bay extension.

Highlights • Extreme value theory approach is applied to before-after safety analysis. • Conflicts of different severity levels are used to estimate GEV distributions. • Odds ratio is calculated based on estimated crashes and most severe conflicts. • Apparent shape change in GEV and significant reduction in odds ratio are found. Abstract There is growing interest in the use of traffic conflicts in before and after safety evaluations because of well-recognized quality and quantity problems associated with historical crash records. Most of these studies apply statistical techniques to compare the number of conflicts before and after the implementation of safety countermeasures. However, to identify the number of conflicts, a specific threshold for various conflict indicators needs to be used and the results of the evaluation can vary significantly depending on the selection of this threshold. As well, there is an issue with how to account for conflict severity in the evaluation. This study proposes adopting the extreme value theory approach to overcome these two issues. The approach was applied to a case of left-turn bay extension at three signalized intersections, and the automated traffic conflict technique was used to identify conflicts with TTC values from the video data collected from treatment sites and matching control sites. Generalized extreme value (GEV) models with different covariates were developed and compared. The results show that there are apparent shape change in the GEV distribution (i.e., from narrow peak up to high severities to wide spread with fewer conflicts at high severity levels) after the treatment, indicating reduction in conflict severity. The safety improvement is further confirmed by the total reduction of 63.9% in estimated crashes. Moreover, with the aid of GEV model, the most severe conflicts that are also rare and random are included into the OR calculation, and a significant reduction of 73.2% is found in the estimated most severe conflicts. [ABSTRACT FROM AUTHOR]