Notwithstanding their importance as the first freight mode choice models estimated with the CFS micro-data, the models estimated in this research have limitations that are worthy acknowledging, in the hope that they could be the outcome in future work.

  1. Lack of sufficient shipment-level data for modes other than truck, rail, and intermodal: As presently configured, the CFS collects a stratified random sample of shipments in the US, that provides a very small sample of shipments sent by inland waterways, air, parcel services, ocean, and pipelines. As a result, the number of observations collected is too small to estimate freight mode choice that consider these modes. Re-designing the CFS to increase the number of shipments that use these modes or repurposing other surveys so that they should be used in conjunction with the CFS data, could provide the data needed.
  2. Lack of readily available data about the operational characteristics of the freight modes: There is no single repository of data about the modal characteristics that could have been seamlessly integrated to the input data used in the modeling process. As a result, the team had to estimate transit times and freight rates for both truck and rail using plausible assumptions and statistical inference techniques. Although this process seemed to have worked well, other important variables—most notably reliability—cannot be estimated a posteriori, and could not be included in the models. Addressing this important issue requires a complementary effort to characterize the performance of the major freight modes, at the time the CFS are collected.

Addressing these issues will be a major step forward towards the development of the freight mode choice models that assist policy makers to quantify the effects of various policies on mode shares.

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