On the implications of using composite vehicles in choice model prediction

Abstract

Vehicle choice modelers often use composite alternatives, which are simplified representations of a larger, diverse group of vehicle options—a practice known as choice set aggregation. Although this practice has been justified by computational tractability and data constraints, it can introduce arbitrary changes to choice-share predictions. We isolate and characterize the implications of using composite vehicles for choice prediction, given exogenously determined model parameters. We first identify correction factors needed for composite models to predict choice shares that are consistent with those from models that use the full set of disaggregated elemental alternatives. We then assess the distortion of choice-share predictions under various composite specifications and partial corrections using two case studies based on models in the literature used in transportation and energy policymaking: (1) we examine a logit model without alternative-specific constants (ASCs) and find that the distortion in share predictions due to composite specification is substantial and can be larger than variation due to parameter uncertainty; (2) we examine counterfactual predictions of a nested logit model with ASCs based on the NEMS and LVChoice models and find that composite models using ASCs can mitigate or eliminate distortion in some, but not all, counterfactual scenarios. In particular, the distortion is larger when the scenario significantly affects the differences in elemental membership or utility heterogeneity between composite groups. We provide explicit correction factors for composite models with and without ASCs that can be used to take advantage of the tractability of composite models while ensuring that their choice-share predictions exactly match those of their corresponding elemental models in counterfactual and forecasting scenarios.