  Accurate representation of observed driving behavior is critical for
effectively evaluating safety and performance interventions in simulation
modeling. In this study, we implement and evaluate a safety-based Optimal
Velocity Model (OVM) to provide a high-fidelity replication of safety-critical
behavior in microscopic simulation and showcase its implications for
safety-focused assessments of traffic control strategies. A comprehensive
simulation model is created for the site of study in PTV VISSIM utilizing
detailed vehicle trajectory information extracted from real-time video
inference, which are also used to calibrate the parameters of the safety-based
OVM to replicate the observed driving behavior in the site of study. The
calibrated model is then incorporated as an external driver model that
overtakes VISSIM's default Wiedemann 74 model during simulated car-following
episodes. The results of the preliminary analysis show the significant
improvements achieved by using our model in replicating the existing safety
conflicts observed at the site of the study. We then utilize this improved
representation of the status quo to assess the potential impact of different
scenarios of signal control and speed limit enforcement in reducing those
existing conflicts by up to 23%. The results of this study showcase the
considerable improvements that can be achieved by utilizing data-driven
car-following behavior modeling, and the workflow presented provides an
end-to-end, scalable, automated, and generalizable approach for replicating the
existing driving behavior observed at a site of interest in microscopic
simulation by utilizing vehicle trajectories efficiently extracted via roadside
video inference.
