A Generic Stochastic Hybrid Car-following Model Based on Approximate
  Bayesian Computation
Jiwan Jiang Yang Zhou Xin Wang Soyoung Ahn
Car following (CF) models are fundamental to describing traffic dynamics.
However, the CF behavior of human drivers is highly stochastic and nonlinear.
As a result, identifying the best CF model has been challenging and
controversial despite decades of research. Introduction of automated vehicles
has further complicated this matter as their CF controllers remain proprietary,
though their behavior appears different than human drivers. This paper develops
a stochastic learning approach to integrate multiple CF models, rather than
relying on a single model. The framework is based on approximate Bayesian
computation that probabilistically concatenates a pool of CF models based on
their relative likelihood of describing observed behavior. The approach, while
data-driven, retains physical tractability and interpretability. Evaluation
results using two datasets show that the proposed approach can better reproduce
vehicle trajectories for both human driven and automated vehicles than any
single CF model considered.