Bayesian networks for constrained location choice modeling using structural restrictions and model averaging
In this work, we propose a Bayesian network approach by using structural restrictions and a model averaging algorithm for modeling the location choice of discretionary activities. In a first stage, we delimit individuals’ location choice which is set by generating an ellipse that uses empirical detour factors and a home-work axis. The choice set is further refined by an individual’s space-time constraints in order to identify the constrained destination choice set. We use structural restrictions and a model averaging method to learn the network structure of the Bayesian network in order to predict the heuristics of individuals’ location selection. The empirical study shows the proposed method can effectively obtain Bayesian networks with a consistent dependency structure. The empirical study suggests activity schedule factors significantly influence location choice decisions.