Modeling Car-Following Heterogeneities by Considering Leader–Follower Compositions and Driving Style Differences

Abstract

To better understand the behavioral heterogeneities of human-operated vehicles, the paper proposes a method to distinguish car-following behaviors in specific leader–follower contexts. Using the Next-Generation Simulation dataset, the car-following data are first classified into four leader–follower compositions, namely, truck–car, car–car, car–truck, and truck–truck. Based on the classified data, we calibrate the parameters of a few well-known car-following models, including Full Velocity Difference model, Intelligent Driver Model, and Gazis–Herman–Rothery model. Principal component analysis and clustering analysis are then applied to the calibrated parameters to discover the behavioral patterns and to find the probabilistic distributions of the parameters for the classified car-following (CCF) models. Simulation results show that compared with the unified car-following models, the estimation errors of calibrated CCF models are reduced by 20.79% to 49.05%, which indicates that the proposed method provides a more accurate description of car-following heterogeneities. The proposed framework could help highway traffic operators better know the traffic users.