@article{ref1,
title="Regimes in social–cultural events-driven activity sequences: Modelling approach and empirical application",
journal="Transportation research part A: policy and practice",
year="2009",
author="Timmermans, Harry and Arentze, Theo",
volume="43",
number="4",
pages="311-322",
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<p />",
language="en",
issn="0965-8564",
doi="10.1016/j.tra.2008.11.010",
url="http://dx.doi.org/10.1016/j.tra.2008.11.010"
}