Five papers on large scale dynamic discrete choice models of transportation
Sammanfattning: Travel demand models have long been used as tools by decision makers and researchers to analyse the effects of policies and infrastructure investments. The purpose of this thesis is to develop a travel demand model which is: sensitive to policies affecting timing of trips and time-space constraints; is consistent with microeconomics; and consistently treats the joint choice of the number of trips to perform during day as well as departure time, destination and mode for all trips. This is achieved using a dynamic discrete choice model (DDCM) of travel demand. The model further allows for a joint treatment of within-day travelling and between-day activity scheduling assuming that individuals are influenced by the past and considers the future when deciding what to do on a certain day.Paper I develops and provides estimation techniques for the daily component of the proposed travel demand model and present simulation results provides within sample validation of the model. Paper II extends the model to allow for correlation in preferences over the course of a day using a mixed-logit specification. Paper III introduces a day-to-day connection by using an infinite horizon DDCM. To allow for estimation of the combined model, Paper III develops conditions under which sequential estimation can be used to estimate very large scale DDCM models in situations where: the discrete state variable is partly latent but transitions are observed; the model repeatedly returns to a small set of states; and between these states there is no discounting, random error terms are i.i.d Gumble and transitions in the discrete state variable is deterministic given a decision.Paper IV develops a dynamic discrete continuous choice model for a household deciding on the number of cars to own, their fuel type and the yearly mileage for each car. It thus contributes to bridging the gap between discrete continuous choice models and DDCMs of car ownership.Infinite horizon DDCMs are commonly found in the literature and are used in, e.g., Paper III and IV in this thesis. It has been well established that the discount factor must be strictly less than one for such models to be well defined.Paper V show that it is possible to extend the framework to discount factors greater than one, allowing DDCM's to describe agents that: maximize the average utility per stage (when there is no discounting); value the future greater than the present and thus prefers improving sequences of outcomes implying that they take high costs early and reach a potential terminal state sooner than optimal.
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