Modeling safety-critical driver behavior at signalized intersections needs to account for the driver's planned decision process, where a driver executes a plan to avoid collision in multiple time steps. Such a process can be embedded in the Optimal Velocity Model (OVM) that traditionally assumes that drivers base their "mental intention" on a distance gap only. We propose and evaluate a data-driven OVM based on real-time inference of roadside traffic video data. First, we extract vehicle trajectory data from roadside traffic footage through our advanced video processing algorithm (VT-Lane) for a study site in Blacksburg, VA, USA. Vehicles engaged in car-following episodes are then identified within the extracted vehicle trajectories database, and the real-time time-to-collision (TTC) is calculated for all car-following instances. Then, we analyze the driver behavior to predict the shape of the underlying TTC-based desired velocity function. A clustering approach is used to assess car-following behavior heterogeneity and understand the reasons behind outlying driving behaviors at the intersection to design our model accordingly. The results of this assessment show that the calibrated TTC-based OVM can replicate the observed driving behavior by capturing the acceleration pattern with an error 20% lower than the gap distance-based OVM.

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