Modeling car-following behaviors using a driving style–based Bayesian model averaging Copula framework in mixed traffic flow

Abstract

As a fundamental driving behavior, the accurate modeling of car-following (CF) dynamics is essential for improving traffic flow and advancing autonomous driving technologies. Due to the stochastic nature of CF behaviors, the CF model parameters often exhibit heterogeneity (multimodal trends), distribution uncertainty, and parameter correlations. Most studies have examined correlations among CF model parameters, assuming deterministic marginal distributions, and investigated heterogeneity through driving behavior indicators. However, distribution uncertainty and multimodal trends in CF model parameter characteristics remain insufficiently explored. To address this challenge, this study proposes a driving style–based Bayesian model averaging Copula (DS-BMAC) framework that simultaneously accounts for heterogeneity, distribution uncertainty, and parameter correlations in CF behavior modeling. Using the intelligent driver model (IDM) as a representative example, its parameters are calibrated using CF trajectory data extracted from the Waymo open motion data set. Based on these calibrated IDM parameters, a multivariate Gaussian mixture model is employed to categorize three distinct driving styles, capturing heterogeneity. Subsequently, a Bayesian model average Copula approach is applied to address distribution uncertainty and parameter correlations. Deterministic and multivehicle ring road simulations were conducted to assess the effectiveness of the proposed DS-BMAC framework. The results demonstrate that the DS-BMAC framework provides a precise characterization of CF model parameters and effectively reproduces microscopic CF behaviors compared to other approaches. Additionally, the DS-BMAC framework offers a realistic representation of traffic flow dynamics. The research findings are valuable for understanding mixed traffic flow dynamics and for developing CF decision-making models for autonomous vehicles and advanced driver-assistance systems.