Bayesian calibration of car-following models

Bayesian calibration of car-following models

Van Hinsbergen, C.P.IJ.
Van Lint, H.W.C.
Hoogendoorn, S.P.
Van Zuylen, H.J.

Civil Engineering and Geosciences

2010-01-28

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. When considered over multiple drivers, the evidence represents probabilities for different models as a whole. These model probabilities can be used in a micro simulation, where for each driver first a model is drawn according to these probabilities, after which parameters are drawn from the posterior distribution for each parameter of that model that were obtained when calibrating the model. In a test case on actual data the Bayesian evidence indeed reveals inter-driver differences and it is shown how these differences can quantitatively be assessed

Calibration, car-following model
longitudinal driver behavior
Bayesian evidence
interdriver differences

https://doi.org/10.4233/uuid:6c0d4f56-9ac4-4ce8-9d17-fc4f6a3342d3

IFAC

12th IFAC symposium on transportation systems, Redondo Beach, Sept. 2009

Institutional Repository

conference paper

(c)2009 Van Hinsbergen, C.P.IJ.; Van Lint, H.W.C.; Hoogendoorn, S.P.; Van Zuylen, H.J.