A cross-sectional safety evaluation approach using generalized extreme value models: A case of right-turn safety treatment.

There has been an increase in the use of the extreme value theory (EVT) approach for conflict-based crash risk estimation and its application such as conducting the evaluation of safety countermeasures. This study proposes a cross-sectional approach for evaluating the effectiveness of a right-turn safety treatment using a conflict-based EVT approach. This approach combines traffic conflicts of different sites at the same period and develops the generalized extreme value (GEV) models. It introduces treatment as a dummy variable for estimating the treatment effects and adds traffic-related and conflict severity-related variables to account for unobserved confounding factors between sites. The approach was applied to a case of right-turn safety treatment at two signalized intersections in Nanjing, China. Conflict indicators (i.e., TTC, PET) and potential influencing factors of E-bike-heavy vehicle (EB-HV) right-turn interactions were extracted from aerial video data. A series of GEV models were developed considering different combinations of covariates and their link to the model parameters. Moreover, site GEV models were developed separately for each site to compare the treatment effects across different models. Based on the best-fit models, the results indicate significant safety improvements after implementing the right-turn safety treatment. In addition, the results also show that the cross-sectional GEV models indicate a significant reduction in the number of high-severity conflicts and lowering overall crash risk attributed to the treatment highlighting the applicability of the GEV cross-sectional models in evaluation safety treatments.