Bayesian networks for multimodal mode choice behavior modelling: a case study for the cross border workers of Luxembourg.

Reducing car use and promoting public transport in the cross border area of Luxembourg has become a priority for sustainable development of the Greater region. In this study, we analyze daily mobility mode choice behavior of these cross border workers, in particular, focusing on their multimodal mode choices (e.g. park and ride mode choice) and on their trip chaining behavior. A rule-based approach based on Bayesian networks is proposed to capture the non-linear effects of related determinants/constraints on individuals' mode choice behavior. The result shows the propose Bayesian network has a competitive performance compared with classical discrete choice models with reasonable good corrected prediction rates.