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
applies Bayesian techniques to the calibration of car-following models, where prior
distributions on each model parameter are converted to posterior distributions. The priors
and posteriors are then used to calculate the so-called 'evidence', which can be used to
quantitatively assess how well different models explain one driver's car-following behavior …