Regimes in social-cultural events-driven activity sequences: modeling approach and empirical application

T.A. Arentze, H.J.P. Timmermans

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)311-322
Number of pages12
JournalTransportation Research. Part A: Policy and Practice
Issue number4
Publication statusPublished - 2009


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