Learning from geometry-aware near-misses to real-time COR: A spatiotemporal grouped random GEV framework (2509.02871v1)

Abstract: Real-time prediction of corridor-level crash occurrence risk (COR) remains challenging, as existing near-miss based extreme value models oversimplify collision geometry, exclude vehicle-infrastructure (V-I) interactions, and inadequately capture spatial heterogeneity in vehicle dynamics. This study introduces a geometry-aware two-dimensional time-to-collision (2D-TTC) indicator within a Hierarchical Bayesian spatiotemporal grouped random parameter (HBSGRP) framework using a non-stationary univariate generalized extreme value (UGEV) model to estimate short-term COR in urban corridors. High-resolution trajectories from the Argoverse-2 dataset, covering 28 locations along Miami's Biscayne Boulevard, were analyzed to extract extreme V-V and V-I near misses. The model incorporates dynamic variables and roadway features as covariates, with partial pooling across locations to address unobserved heterogeneity. Results show that the HBSGRP-UGEV framework outperforms fixed-parameter alternatives, reducing DIC by up to 7.5% for V-V and 3.1% for V-I near-misses. Predictive validation using ROC-AUC confirms strong performance: 0.89 for V-V segments, 0.82 for V-V intersections, 0.79 for V-I segments, and 0.75 for V-I intersections. Model interpretation reveals that relative speed and distance dominate V-V risks at intersections and segments, with deceleration critical in segments, while V-I risks are driven by speed, boundary proximity, and steering/heading adjustments. These findings highlight the value of a statistically rigorous, geometry-sensitive, and spatially adaptive modeling approach for proactive corridor-level safety management, supporting real-time interventions and long-term design strategies aligned with Vision Zero.