Estimating a model of dynamic activity generation based on one‐day observations: Method and results

In this paper we develop and explore an approach to estimate dynamic models of activity generation on one‐day travel‐diary data. Dynamic models predict multi‐day activity patterns of individuals taking into account dynamic needs as well as day‐varying preferences and time‐budgets. We formulate an ordered‐logit model of dynamic activity‐agenda‐formation decisions and show how one‐day observation probabilities can be derived from the model as a function of the model’s parameters and, with that, how parameters can be estimated using standard loglikelihood estimation. A scale parameter cannot be identified because information on within‐person variability is lacking in one‐day data. An application of the method to data from a national travel survey illustrates the method. A test on simulated data indicates that, given a pre‐set scale, the parameters can be identified and that estimates are robust for a source of heterogeneity not captured in the model. This result indicates that dynamic activity‐based models of the kind considered here can be estimated from data that are less costly to collect and that support the large sample sizes typically required for travel‐demand modeling. We conclude therefore that the proposed approach opens up a way to develop large‐scale dynamic activity‐based models of travel demand.