Modeling Intra-Driver Behavioral Adaptation through Risk Sensitivity and Regime Transitions: Task Saturation Car-Following Model

Over the last decade, there has been a growing trend toward integrating human factors (HF) into traffic flow models to better understand the complexities of human behavior and its effect on traffic dynamics. This study seeks to advance this trend by bridging the gap between traditional car-following models and the inherent variability in human driving behavior. By incorporating these elements, the models provide valuable insights into how driver behavior adaptation and risk-taking influence traffic flow dynamics. This study proposes a model, the Intelligent Driver Model with Task Saturation (IDMTS), that integrates behavioral adaptations and risk-taking strategies into the established Intelligent Driver Model, enriched by a cognitive layer based on Fuller’s Task Capability Interface model. This amalgamation enables interplay between driver behavior adaptation as the driving task saturates and drivers’ risk-taking strategy. First, steady state equilibria and fundamental diagrams of IDMTS are derived, and local and string stability are analyzed under rational driving constraints. Then, the IDMTS is calibrated and validated on three complementary data sets: controlled driving simulator experiments with normal and distracted driving, and naturalistic car-following trajectories from the Next Generation Simulation I-80 and Milan Trajectories data sets. The model is investigated for behavioral soundness and numerical soundness. The results demonstrate that the model effectively incorporates endogenous heterogeneity in driving behavior under varying task loads. The model generates two plausible HFs: (1) risk-taking; and (2) behavior adaptation. The findings of this study suggest a step toward a more realistic representation of driving behavior adaptation and risk-taking strategies.