Shipment-level models are typically estimated as Discrete Choice Models (DCMs). DCMs try to capture the decision-making behavior of the agent in charge of making decisions on mode/vehicle choice, using Random Utility Theory (Manski 1973, Manski 1977). DCMs express the probability of choosing a mode/vehicle as a function of such variables as shipment size, cost and time for each mode/vehicle alternative, commodity type, and the attributes of the shipper. These utility functions, because of their empirical nature, capture the effects of the key independent variables without having to collect highly detailed data about each and every variable that could influence freight mode/vehicle choices.
The data needed to estimate and use these modes are disaggregate, typically at the shipment level. The power of DCMs resides in their ability to consider the effect of spatial variables, e.g., the distance between an establishment and the closest rail terminal, or the effects of the frequency of rail service on the waiting times and ultimately the mode/vehicle decision. DCMs provide a better platform to capture the interdependence between shipment size and the mode choice (endogeneity), and multiple ways to consider the interactions among various vehicle types for a given mode. The methodological alternatives include: nested logit models with choices of mode/vehicles and shipment sizes (Chiang et al. 1981); advanced models that explicitly treat shipment size as a continuous model that is embedded in the mode/vehicle choice model (McFadden et al. 1986); and models that use instrumental variable techniques (Holguín-Veras 2002).
It should be noted that the CFS public use data file released by the Bureau of Transportation Statistics and the Bureau of the Census provide much of the data needed for estimation and application of discrete choice models to the study of freight mode choice. However, since data about freight vehicles are not collected, the CFS data cannot be used to study freight vehicle choice (particularly for trucking).