| Summary: | The research presented in this paper proposes and develops a new car-following model, which we term 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 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 profile while converging to steady states; and (5) it explicitly captures driver perception and control inaccuracies and errors. Besides describing the methodology and the role of each of the model parameters, this study evaluates the performance of the new model using a naturalistic driving dataset. A brief comparative analysis was performed to validate the model with regard to replicating empirical driver and vehicle behavior. Through a quantitative and qualitative evaluation, the proposed FR model demonstrates a significant decrease in the modeling error when compared to the original RPA model and generates trajectories that are highly consistent with empirically observed car-following behavior. Keywords: Car-following, Vehicle dynamics, Collision avoidance, Naturalistic driving data, Vehicle trajectory
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