Showing papers by "Daniel McFadden published in 2002"

Hybrid choice models : Progress and challenges

Abstract: We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.

Discrete choice models incorporating revealed preferences and psychometric data

Abstract: This paper proposes a methodology for incorporating psychometric data such as stated preferences and subjective ratings of service attributes in econometric consumer's discrete choice models. Econometric formulation of the general framework of the methodology is presented, followed by two practical submodels. The first submodel combines revealed preference (RP) and stated preference (SP) data to estimate discrete choice models. The second submodel combines a linear structural equation model with a discrete choice model to incorporate latent attributes into the choice model using attitudinal data as their indicators. Empirical case studies on travel mode choice analysis demonstrate the effectiveness and practicality of the methodology.

The path to discrete-choice models

Abstract: Nobel economist Daniel McFadden explains how he devised the method of discrete-choice modeling to help explain why travelers choose different modes and destinations. The traditional analysis depended on gravity models, which aggregate data about people's travel decisions in order to establish rates of flow between pre-determined zones. This is network-based rather than based on individuals' situations. In 1971, McFadden proposed to forecast travel demand based on individual travel choices. He developed multinomial logit models to carry out discrete-choice analysis, or the theory of random utility maximization. The beginning of the Bay Area Rapid Transit (BART) system presented a unique chance to test the models as the new mode was developed. The model correctly predicted that BART would start out carrying roughly 6% work trips. However, some assumptions proved incorrect, especially the willingness of people to walk to transportation and the importance of buses.

ACCESS Magazine Spring 2002

Abstract: Author(s): Chen, Chao; De Valois, Karen; Disch, Michael; Dowall, David; Mcfadden, Daniel L.; Regan, Amelia; Shoup, Donald; Takeuchi, Tatsuto; Varaiya, Pravin | Editor(s): Webber, Melvin M.; Lave, Charles; Curry, Melanie; Sutch, Diane

Hybrid Choice Models: Progress and Challenges

Abstract: We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological variables, heterogeneity, and latent segmentation. Both progress and challanges related to the development of the hybrid choice model are presented.