  This study develops a real-time framework for estimating the risk of
near-misses by using high-fidelity two-dimensional (2D) risk indicator
time-to-collision (TTC), which is calculated from high-resolution data
collected by autonomous vehicles (AVs). The framework utilizes extreme value
theory (EVT) to derive near-miss risk based on observed TTC data. Most existing
studies employ a generalized extreme value (GEV) distribution for specific
sites and conflict types and often overlook individual vehicle dynamics
heterogeneity. This framework is versatile across various highway geometries
and can encompass vehicle dynamics and fidelity by incorporating covariates
such as speed, acceleration, steering angle, and heading. This makes the risk
estimation framework suitable for dynamic, real-world traffic environments. The
dataset for this study is derived from Waymo perception data, encompassing six
sites across three cities: San Francisco, Phoenix, and Los Angeles. Vehicle
trajectory data were extracted from the dataset, and near-miss frequencies were
calculated using high-fidelity 2D TTC. The crash risk was derived from observed
near misses using four hierarchical Bayesian GEV models, explicitly focusing on
conflicting pairs as block minima (BM), which revealed that crash risk varies
across pairs.The proposed framework is efficient using a hierarchical Bayesian
structure random parameter (HBSRP) model, offering superior statistical
performance and flexibility by accounting for unobserved heterogeneity across
sites. The study identifies and quantifies that the most hazardous conditions
involve conflicting vehicle speeds and rapid acceleration and deceleration,
significantly increasing crash risk in urban arterials.
