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

Theo Arentze and Harry Timmermans

Transportation Research Part A: Policy and Practice, 2009, vol. 43, issue 4, 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 (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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