Citation

Arentze T, Timmermans H. Transp. Res. A Policy Pract. 2009; 43(4): 311-322.

Copyright

(Copyright © 2009, Elsevier Publishing)

DOI

10.1016/j.tra.2008.11.010

PMID

unavailable

Abstract

In this study we propose and apply a Bayesian-network model to predict and analyse the factors that influence activity-travel sequences that are triggered by social–cultural events. The study is motivated by the intention to examine the wider context in which activity-travel decisions are made and to model such decisions under longitudinal time horizons. We assume that social events trigger a series of interrelated activities and corresponding trips. Data about events and related activities are collected using a month-diary and involving a large sample of households in the Eindhoven region, The Netherlands. A learning algorithm is applied to derive a Bayesian-network model from the event diary. The results show that indeed many travel choices are influenced by particular events, that these influences vary by socio-demographic variables and that the learned Bayesian-network model is able to represent these interdependencies among all these variables. We demonstrate how the model can be used to predict event-driven activity-travel sequences in a micro-simulation. Keywords: Activity-based models; Events; Bayesian-networks; Network-learning; Regimes