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A Validation Study of the Fadhloun-Rakha Car-following Model

: The research presented in this paper investigates and validates the performance of a new car-following model (the Fadhloun-Rakha (FR) model). The FR model incorporates the key components of the Rakha-Pasumarthy-Adjerid (RPA) model in that it uses the same steady-state formulation, respects vehicle dynamics, and uses very similar collision-avoidance strategies to ensure safe following distances between vehicles. The main contributions of the FR model over the RPA model are the following: (1) it explicitly models the driver throttle and brake pedal input; (2) it captures driver variability; (3) it allows for shorter than steady-state following distances when following faster leading vehicles; (4) it offers a much smoother acceleration profiles; and (5) it explicitly captures driver perception and control inaccuracies and errors. In this paper, a naturalistic driving dataset is used to validate the FR model. Furthermore, the model performance is compared to that of five widely used car-following models, namely: the Wiedemann model, the Frietzsche model, the Gipps model, the RPA model and the Intelligent Driver Model (IDM). A comparative analysis between the different model outputs is used to determine the performance of each model in terms of its ability to replicate the empirically observed driver/vehicle behavior. Through quantitative and qualitative evaluations, the proposed FR model is demonstrated to significantly decrease the modeling error when compared to the five aforementioned models and to generate trajectories that are highly consistent with empirically observed driver following behavior.