Driver behavior classification for stop-n-go traffic via machine learning based driver model

Modeling driver behavior in car-following scenarios is crucial for traffic simulation and the development of intelligent transportation systems. In dense urban environments, particularly in stop-and-go traffic, capturing the nuances of individual driving styles is essential for realistic predictions. This paper addresses the limitations of the well-established Optimal Velocity Model (OVM) that fails to identify the drivers’ behavior and yields physically unrealistic parameters. A new driver model is developed via collecting detailed vehicle motion data from stop-and-go urban traffic using image processing. We introduce a speed policy-based driver model via the fitting of a universal differential equation to the data. Then, the identified policy is approximated by a simple, piecewise smooth function with a small number of physically interpretable parameters. These parameters and the driver reaction times are identified from the traffic data with excellent consistency. Using the principal component analysis, driving behaviors are characterized. Finally, numerical simulations of vehicle strings confirm that the proposed model is more applicable and robust for simulating stop-and-go traffic than the traditional OVM.