Bayesian hierarchical modeling of traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach

This study presents a Bayesian hierarchical model to estimate crashes from traffic conflict extremes in a non-stationary context. The model combines a peak over threshold approach with non-stationary thresholds in terms of regression quantiles and covariate-dependent parameters of the generalized Pareto distribution. The developed model was applied to estimate rear-end crashes from traffic conflicts of the same type collected from four signalized intersections. The conflicts were measured by the modified time to collision (MTTC) and traffic volume, shock wave area, average shock wave speed, and platoon ratio of each signal cycle were employed as covariates. Thresholds corresponding to quantiles ranging from 80% to 95% were tested and the threshold stability plot indicated the 90% quantile was reasonable. Threshold excesses were then declustered at the signal cycle level, and the remained ones were used to develop the Bayesian hierarchical generalized Pareto distribution models (BHM_GPD). The model estimation results show that accounting for non-stationarity significantly improves the model fit. As well, the best fitted model generated accurate crash estimates with relatively narrow confidence intervals. The developed BHM_GPD model was also compared to the Bayesian hierarchical generalized extreme value model (BHM_GEV). The results show that the two models generate comparable crash estimates in terms of accuracy, but the crash estimates from the BHM_GPD model are generally mor...
Source: Analytic Methods in Accident Research - Category: Accident Prevention Source Type: research