Modeling travel behavior is a key aspect of demand analysis, where aggregate demand is the accumulation of individuals' decisions. In this chapter, we focus on "short-term" travel decisions. The most important short-term travel decisions include choice of destination for a non-work trip, choice of travel mode, choice of departure time and choice of route. It is important to note that short-term decisions are conditional on long-term travel and mobility decisions such as car ownership and residential and work locations.The analysis of travel behavior is typically disaggregate, meaning that the models represent the choice behavior of individual travelers. Discrete choice analysis is the methodology used to analyze and predict travel decisions. Therefore, we begin this chapter with a review of the theoretical and practical aspects of discrete choice models. After a brief discussion of general assumptions, we introduce the random utility model, which is the most common theoretical basis of discrete choice models. We then present the alternative discrete choice model forms such as Logit, Nested Logit, Generalized Extreme Value and Probit, as well as more recent developments such as Hybrid Logit and the Latent Class choice model. Finally, we elaborate on the applications of these models to two specific short term travel decisions: route choice and departure time choice.
Discrete Choice ModelsWe provide here a brief overview of the general framework of discrete choice models. We refer the reader to Ben-Akiva and Lerman (1985) for the detailed developments.
This paper affords a stylized view of individual consumer choice decision-making appropriate to the study of many marketing decisions. It summarizes issues relating to consideration set effects on consumer judgment and choice. It discusses whether consideration sets really exist and, if so, the factors that affect their composition, structure, and role in decision-making. It examines some new developments in the measurement and modeling of consideration set effects on decision-making. The paper concludes with suggestions for needed research.Most contemporary accounts of human decision-making give a prominent role to simplification. This extends not only to the "process" presumedly used by the decision-maker in reaching a decision, where simplification acknowledges the decision-maker's efforts to make his/her task easier and more functional, but also to the models of that process proposed by those who study decision-making (Wright 1975). Simple models are to be preferred because they are tractable, a fact that is particularly important when the analyst's task is to make predictions for large numbers of consumers. On the other hand, many behavioral scientists have questioned the adequacy of such models as explanation since they often find a process *The authors wish to acknowledge the numerous ideas and perspectives contributed by the other members of the Banff Symposium workshop:
In this thesis, we present a comprehensive framework to estimate and predict timedependent Origin-Destination (O-D) ows. The key feature of this framework is its ability to handle di erent t ypes of information (\measurements") with di erent error characteristics and from di erent sources in a consistent and uni ed manner. The framework is used to address two types of problems { the o ine estimation problem and the real-time estimation and prediction problem. For the o ine estimation problem, we enhance least squares based procedures developed by other researchers. To t h e real-time estimation and prediction problem, we apply state-space modeling techniques to obtain recursive estimation algorithms. Main features of our models are: (a) use of deviations of O-D ows from historical averages as unknown variables (b) modeling of originating trips and destination fractions separately to improve prediction e ciency and (c) introduction to the notion of a stochastic assignment matrix for mapping O-D ows to link counts. The suite of models developed in this thesis is evaluated rigorously using actual tra c data from three di erent sources. Empirical results are promising and indicate the robustness of the proposed framework.
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