Accurate calibration of car-following models is essential for understanding human driving
behaviors and implementing high-fidelity microscopic simulations. This work proposes a …

Car-following behavior modeling is critical for understanding traffic flow dynamics and
developing high-fidelity microscopic simulation models. Most existing impulse-response car …

Driving assistance systems that adapt to an individual driver are essential for avoiding traffic
accidents because there are individual differences in the way of driving. To realize such …

Microscopic roadway traffic simulators, which attempt to mimic real-world driver behaviors,
are based on car-following models and have been widely used as a cost-effective tool for …

The data–driven car–following model can realistically depict driving behavior, and is widely
used in simulating the longitudinal movements of vehicles. However, it has some …

Recent research has revealed that there exist large inter-driver differences in car-following
behavior such that different car-following models may apply to different drivers. This study …

Heterogeneity exists in car following behaviors due to the driver's habit, fatigue, distraction,
or surrounding traffic. This research proposes a method of modeling and reasoning …

This paper discusses the limitations of existing microscopic traffic models in accounting for
the potential impacts of on-ramp vehicles on the car-following behavior of main-lane …

Driving assistance systems are essential technologies to avoid traffic accidents, reduce
traffic jams, and solve environmental problems. Not only observable behavioral data, but …

This paper describes a stochastic modeling approach for predicting driver responses in
highway traffic. Different from existing approaches in the literature, the proposed modeling …