An Application of the Tau-Path Method in Highway Safety

There is growing interest among road safety analysts for using surrogate measures of safety as an alternative to crash counts, especially in contexts where the temporal and/or spatial crash occurrence rate is extremely low. In order to facilitate this paradigm shift, it is useful to demonstrate significant association between conflicts and crashes, and to study how this association might vary by location. We investigate a semiparametric statistical approach called tau-path that enables us to rank locations by decreasing magnitude of the association between crash and conflict counts. We demonstrate the method in the context of pedestrian safety at intersections in Connecticut with variation in several characteristics. Locations with high association between conflicts and crashes were more likely to have exclusive pedestrian phasing and on-street parking. Among these locations, those with high conflict and crash counts were more likely to have on-street parking and be in non-residential areas. The tau-path method can be easily applied to other road safety contexts beyond investigating conflict counts as surrogates for crash counts. This approach is also relevant to general data mining settings where there is a need to identify a subpopulation in which there is a strong association between a pair of variables of interest.