Because of well-recognized quality and quantity problems associated with historical crash data, traffic conflict techniques have been increasingly used in before-after safety analysis in recent years. This study proposes using an extreme value theory (EVT) approach to conduct traffic conflict-based before-after analysis. The capability of providing confident estimation of extreme events by the EVT approach drives the before-after analysis to shift from normal traffic conflicts to more serious conflicts, which are relatively rare but have more in common with actual crashes. The approach is applied to evaluate the safety effects of converting channelized right-turn lanes into smart channels, based on traffic conflicts defined by time to collision (TTC) and collected from three treatment intersections and one control intersection in the city of Penticton, British Columbia. Odds ratios and treatment effects are calculated from extreme-serious conflicts, the frequencies of which are estimated from the generalized Pareto distributions of traffic conflicts with TTC⩽0.5 s. The results show approximately 34% reduction in total extreme-serious conflicts (i.e., combining merging conflicts and rear-end conflicts), indicating overall a remarkable safety improvement following the smart channel treatment. This finding is consistent with the analysis result based on traffic conflicts with TTC⩽3.0 s. It is also found that the reduction in extreme-serious merging conflicts is small and insignificant. This is caused by the phenomenon that TTC values of merging conflicts become smaller after the treatment, and it is possibly because drivers become more aggressive with the better view of approaching cross-street traffic provided by the smart channel.
Topics in data analysis using R in extreme value theory
