Trip chains: Theoretical development and empirical analysis

Trip chaining has increased in the last two decades. Yet, existing travel demand models do not account for activity sequencing. In this dissertation we establish two theoretical developments resulting in trip-chain models for single-stop work trip chains. The first development is a deterministic bilevel entropy-maximizing framework which gives as optimality conditions an entropy gravity model. The second development presents a probabilistic threshold theory of spatial interaction trip-chaining behavior which is characterized by mean frequencies representable by threshold gravity models. Entropy gravity models are seen to be an approximation of threshold gravity models. Both theoretical developments are seen as two threads originating from a common behavioral assumption, namely that trip chaining is based on an r-sequential decision making process. This assumption legitimizes the study of single-stop trip chains.;Estimation procedures are presented for both theoretical developments. Maximum likelihood estimates for threshold gravity models are obtained from the modifying scoring procedure. For the estimation of entropy gravity models a new procedure is developed, the sequential modifying scoring procedure. Preliminary empirical analysis based on a home interview survey reveals a very good fit for threshold gravity models. An important empirical finding is that in the presence of the data available, home-to-work and the reverse single-stop trip chains are indistinguishable in the sense that there is no need for separate parameter estimates. This is a very desirable result from the transportation modeler's perspective. Finally, short-term forecasting suggestions are made under reasonable assumptions. Overall, we believe that the theoretical developments proposed in this dissertation suggest a very promising research direction toward trip-chain models readily absorbable into more general travel demand forecasting models.