Identifying behavioral principles underlying activity patterns by means of Bayesian networks

Abstract

Capturing behavioral principles within the context of activity-based travel patterns is of vital importance to build adequate transportation planning models. Of course, there is no solitarily model which is perfectly capable of capturing all behavioral patterns but certain techniques are better suited for it than others. In this paper the technique of Bayesian networks is introduced. Bayesian networks are potentially very strong representation techniques since they are capable of capturing the multidimensional nature of complex decisions. Several arguments are presented which clarify why the presented approach is particularly well suited to identify behavioral patterns. To this end and as an empirical study, several significant findings which might exert influence on the choice of transport mode choice, were extracted from a large number of potential factors, in the context of a large activity diary dataset. Furthermore, the paper shows a detailed sensitivity analysis report which enables a quantitative evaluation.