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

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  • Arentze, Theo
  • Timmermans, Harry

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.

Suggested Citation

  • Arentze, Theo & Timmermans, Harry, 2009. "Regimes in social-cultural events-driven activity sequences: Modelling approach and empirical application," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 311-322, May.
    • Handle: RePEc:eee:transa:v:43:y:2009:i:4:p:311-322
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    1. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
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