Empirical results of effects of various causal factors on car-following behavior.
Taehyung Kim, David J. Lovell, Hyoungsoo Kim, and Cheol Oh
Transportation Research Record 2188, pp. 174–186.
ABSTRACT
Experimentation and analysis methodologies for data intended for
the development of microscopic car-following models are presented.
The data methodology centers on the notion that it is better to sample
a varied group of real drivers when the subjects are unaware that an
experiment is being conducted. This contrasts with the methods used to
calibrate most conventional models. The analysis focuses on determining
possible causal factors, beyond the kinematic variables most often
used, that could play a role in real traffic. These factors include various
human characteristics (e.g., gender and distractions related to in-vehicle
conditions like telephoning and vehicle occupancy); traffic and road
characteristics (e.g., type of vehicle, congestion level, and location of
driving lane); and environmental characteristics (e.g., time of day and
weather conditions). The paper shows the magnitudes of the relationships
that are discovered and offers suggestions as to how these results
might contribute to the development of better models.