Real-time corridor-wide crash-occurrence risk (COR) prediction is challenging because existing near-miss extreme value theory (EVT) models often oversimplify collision geometry, neglect vehicle-infrastructure (V-I) interactions, and inadequately account for spatial heterogeneity in traffic and roadway conditions. This study develops a geometry-aware two-dimensional time-to-collision (2D-TTC) near-miss extraction framework and integrates it with a hierarchical Bayesian grouped random parameter unified generalized extreme value model (HBSGRP-UGEV) to estimate short-term COR in urban corridors. The proposed framework builds on prior grouped EVT formulations while explicitly accommodating both vehicle-vehicle (V-V) and vehicle-infrastructure (V-I) near-miss processes within a unified corridor-wide modeling structure. High-resolution trajectories from the Argoverse-2 dataset were analyzed across 28 sites along Miami's Biscayne Boulevard to extract extreme near-miss events. The model incorporates vehicle dynamics and roadway features as covariates, with partial pooling across segments and intersections to capture corridor-wide heterogeneity. Results indicate that the HBSGRP-UGEV framework outperforms the fixed-parameter HBSFP-UGEV model, reducing the deviance information criterion (DIC) by up to 7.5 percent for V-V interactions and 3.1 percent for V-I interactions. Predictive validation using receiver operating characteristic area under the curve (ROC-AUC) demonstrates strong classification performance, with values of 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.

翻译：实时走廊范围碰撞发生风险预测具有挑战性，因为现有基于近碰撞事件的极值理论模型常过度简化碰撞几何形态、忽略车辆-基础设施交互作用，且未能充分考量交通与道路条件的空间异质性。本研究开发了一种几何感知的二维碰撞时间近碰撞事件提取框架，并将其与分层贝叶斯分组随机参数统一广义极值模型相融合，用于估计城市走廊的短期碰撞风险。所提框架在既有分组极值理论模型基础上，通过统一的走廊范围建模结构，同时显式处理车辆-车辆与车辆-基础设施两类近碰撞过程。基于Argoverse-2数据集的高分辨率轨迹数据，对迈阿密比斯坎大道沿线28个观测点进行分析以提取极端近碰撞事件。模型将车辆动力学特征与道路几何特征作为协变量，并通过路段与交叉口间的部分池化效应捕捉走廊范围的异质性。结果表明，HBSGRP-UGEV框架优于固定参数HBSFP-UGEV模型，使车辆-车辆交互的偏差信息准则降低达7.5%，车辆-基础设施交互降低3.1%。基于受试者工作特征曲线下面积的预测验证显示模型具有强分类性能：车辆-车辆路段为0.89，车辆-车辆交叉口为0.82，车辆-基础设施路段为0.79，车辆-基础设施交叉口为0.75。