Abstract In this paper we extend and explore a method to estimate dynamic models of activity generation on 1-day travel diary data. Dynamic models predict longitudinal activity patterns of individuals taking into account dynamic needs as well as day-varying preferences and time-budgets. We show how 1-day work time observation probabilities can be derived from proposed dynamic models as a function of a model’s parameters and, with that, how budget-constraints and activity parameters can be estimated using standard loglikelihood estimation. The results of an application on data from a national travel survey are well interpretable. Moreover longitudinal activity patterns predicted by the model have approximately the same statistical characteristics as the 1-day sample data from the survey. We conclude therefore that the proposed method opens up a way to develop a next generation of dynamic activity-based models of travel demand.
A dynamic model of time-budget and activity generation: Development and empirical derivation
Tramsportation Research, Part C: Emerging Technologies ; 19 , 2 ; 242-253
2010-01-01
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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