Overview of Model Results

Three different types of models were estimated as part of this research:

1)      Shipment size models: These models express shipment size as a function of the great circle distance (GCD) between the shipper and the receiver. Although they were originally estimated to eliminate the endogeneity between the decisions of shipment size and freight mode (or vehicle) choice, these models are very useful in cases where the shipment size data are not reliable or available. As a result, they can be used in applications of both market share and shipment-level models.

2)      Market-share models of freight mode choice: These models estimate the percent share of the freight modes of a given market that is expected to select either truck or rail. The models presented in this report use generalized cost as the key independent variable. Although the team conducted extensive testing of models using transit times and rates as separate independent variables, none was found to be statistically significant and conceptually valid.

3)      Shipment-level models of freight mode choice: These models estimate the probability that a shipment would be sent by rail or truck as a function of: 1) the estimates of the actual shipment size provided by the shipment size models (to remove the correlation between shipment size and freight mode choice); and 2) the variables that characterize the operational performance of the freight modes (i.e., generalized cost, transit time, and freight rates). The disaggregate (shipment-level) models are further classified into three types: 1) unweighted models; 2) weighted based on domestic cargo; and 3) weighted based on total (domestic and international) cargo. The unweighted models estimate the freight mode choice assuming that the CFS actually estimates the modal shares in the US. The weighted models ensure that the freight mode choice data reflect the actual mode share published by Freight Analysis Framework (FAF) version 4 (Freight Analysis Framework 2018).

Table 19 provides the count of total number of models (the good, bad and the ugly) estimated for the freight mode choice analysis. The sections below explain the various freight mode choice models in detail.

Table 19

As shown, the number of models estimated are typically different. This is the result of two different factors. The first is the effect of disclosure constraints from the Census Bureau to prevent the release of models that contain confidential information. Since the aggregate models have a better chance of passing the disclosure requirements, a larger number of aggregate models could be released. The second factor was the combination of the computation time required by the shipment-level models, which typically took days and sometimes weeks to finish, and the restriction of the maximum number of programs that could be run by a researcher at the Census Bureau servers.

The freight mode choice models (both aggregate and disaggregate) are grouped in two categories:

1)      Models based on transit time and freight rates: These models explicitly consider the effects of transit times and freight rates by mode. The main benefit of these models is that they enable the estimation of the implicit Value-of-Time by commodity type.

2)      Models based on generalized cost: These models used a composite measure of cost that includes freight rates and time. The latter variable is multiplied by the intrinsic cargo value. These models are useful in cases where there are no models that consider transit times and freight rates are significant.

Table 20 shows the summary counts of the various types of models estimated together with the numbers of the table where the results can be found. In general terms, the models that used generalized costs (particularly, with 5% opportunity cost) were found to work better than the models that considered freight rates and transit times separately as independent variables.

Table 20

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