The need for cost-effective research techniques in transport has led to increasing use of stated-preference data, as well as the development of mixed models, based on multiple-data sources. Different types of data, as used in such models, may have different accuracy and sources of error. Such differences exist between revealed-preference and stated-preference data. The magnitude and source of errors in different types of data will be reflected in differences in both the measured components and in the unmeasured components (variance) captured in the corresponding models. When choice models are estimated on multiple-data sources, such as various types of revealed- and stated-preference data, these differences must be taken into account explicitly in the specification of the model structure and the utility functions. The chapter discusses the main issues involved in the estimation and application of discrete-choice models with mixed data, covering: (a) the theoretical framework for combining data sources; (b) the specification of the model and the likelihood function; (c) the suitability and accessibility of different estimation techniques; (d) a case study that compares binary probit- and logit-estimation methods; (e) a case study that incorporates a new mode into a nested-logit model; (f) issues involved in interpreting and applying mixed-model results. In particular, the paper describes a new approach for estimating models on mixed-data sources using the "tree-logit" estimation technique. (A)
Abstract