AbstractCar-following models are calibrated to account for various driver behaviors such as speed and space headway. Because drivers do not all drive the same way, they are typically classified based on their level, or profile, of aggressiveness. This approach to model calibration assumes that a single set of car-following parameters applies to an individual driver consistently, that is, during the entire driving period a study considers. The purpose of this research is to challenge that assumption by analyzing the heterogeneity and inconsistency of driver behaviors in different traffic conditions. A total of 3,262 urban expressway car-following periods from 51 drivers were extracted from the Shanghai Naturalistic Driving Study database. The intelligent driver model (IDM) was selected as this study’s car-following model for its favorable performance in highly dynamic situations; its parameters, that is, desired speed, desired time headway, maximum acceleration, comfortable deceleration, acceleration exponent, and minimum spacing, were calibrated. To reflect the effect of traffic conditions, the car-following periods were categorized into three regimes from low-speed to high-speed. Two-way analysis of variance was used to evaluate heterogeneity and inconsistency in the car-following parameters. The findings of this study show that (1) the main significant variable that distinguished driver profile was desired time headway, while the comfortable acceleration was commonly affected by both driver profile and speed regime; (2) variability of individual driver parameter estimates greatly depended on the traffic regime (low-, medium-, and high-speed), showing that driver behavior was highly influenced by traffic conditions; and (3) speed information was demonstrated to be a useful tool by which to divide car-following periods for modeling, as the car-following parameters exhibited distinct differences among the regimes in the varied traffic environment.