  Compute and memory constraints have historically prevented traffic simulation
software users from fully utilizing the predictive models underlying them. When
calibrating car-following models, particularly, accommodations have included 1)
using sensitivity analysis to limit the number of parameters to be calibrated,
and 2) identifying only one set of parameter values using data collected from
multiple car-following instances across multiple drivers. Shortcuts are further
motivated by insufficient data set sizes, for which a driver may have too few
instances to fully account for the variation in their driving behavior. In this
paper, we demonstrate that recent technological advances can enable
transportation researchers and engineers to overcome these constraints and
produce calibration results that 1) outperform industry standard approaches,
and 2) allow for a unique set of parameters to be estimated for each driver in
a data set, even given a small amount of data. We propose a novel calibration
procedure for car-following models based on Bayesian machine learning and
probabilistic programming, and apply it to real-world data from a naturalistic
driving study. We also discuss how this combination of mathematical and
software tools can offer additional benefits such as more informative model
validation and the incorporation of true-to-data uncertainty into simulation
traces.
