PROGRESS IN GEOGRAPHY ›› 2015, Vol. 34 ›› Issue (10): 1275-1287.doi: 10.18306/dlkxjz.2015.10.008

• Model and Remote Sensing Application • Previous Articles     Next Articles

Research progress of discrete choice models

Can WANG1(), De WANG1, Wei ZHU1, Shan SONG2   

  1. 1. College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
    2. Graduate School of Environmental Studies, Nagoya University, Nagoya 464-8601, Japan
  • Received:2015-03-01 Accepted:2015-07-01 Online:2015-10-20 Published:2015-10-20

Abstract:

This article takes the general principles and application values of the discrete choice model system as a departure point and summarizes the classical model forms with respect to their basic theories and typical applications. Important latest developments are also introduced. Multinomial logit (MNL) model is the basis of the discrete choice model system, with the advantages of simplicity, reliability, and easy implementation. However, it also has some inherent theoretic defects, which led to the need for more refined models. Nested logit model is usually used to deal with problems of correlation among alternatives, no-choice alternative, and data enrichment. Its more general form is the generalized extreme value (GEV) model system; mixed logit model is suitable for handling random preference and some kinds of correlation problems, such as correlation among alternatives, panel data, random coefficients, and data for enrichment. A similar model form named latent class model is also widely used. Multinomial probit (MNP) model is highly flexible. However, its application is limited due to the complexity of model specification and very high computation demands. With regard to the new development of discrete choice model system, four important areas are introduced. These include complex new models derived from the combination of classical models; models suitable for dealing with revealed preference/stated preference (RP/SP), ordered, ranked, and multiple choice data; models based on bounded rationality choice which is more close to reality; and models considering the spatiotemporal background of choice.

Key words: discrete choice model, refining, applicability, new trends