Demand for Freight Transportation in the U.S.: A High-Level View

Research on freight transportation has seen a tremendous increase in the last decades, yet it lags behind that on passenger travel, particularly at a macro-level suitable for nation-wide policy analysis. A key challenge in freight demand modeling is the availability of data on key drivers of demand such as cost, time, and trip length which usually is proprietary and expensive. Moreover freight data available to the public is usually heterogeneous and published by a number of different bodies. In this study we integrate many publicly-available datasets on these attributes into a consistent database describing freight movement at the U.S. level. We then use this dataset to calibrate a discrete-choice model of the shares of major single modes truck, rail, and air, and compare our results with other similar exercises from the transportation economics literature. We thus present an analysis of the effect of generalized transportation costs and infrastructure quality captured by travel time on modal split at the national level. We conclude with recommendations regarding freight transportation data that arise from the insights offered by this exercise for policy-makers and practitioners.