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Modeling ocean, rail, and truck transportation flows to support policy analysis

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Maritime Economics & Logistics Aims and scope


Freight transportation represents about 9.5% of GDP in the U.S., it is responsible for about 8% of greenhouse gas emissions, and supports the import and export of about 3.6 trillion in international trade. It is therefore important that the national freight transportation system is designed and operated efficiently. Hence, this paper develops a mathematical model to estimate international and domestic freight flows across ocean, rail, and truck modes, which can be used to study the impacts of changes in our infrastructure, as well as the imposition of new user fees and changes in operating policies. The model integrates a user equilibrium-based logit argument for path selection with a system optimal argument for rail network operations. This leads to the development of a unique solution procedure that is demonstrated in a large-scale analysis focused on all intercity freight and U.S export/import containerized freight. The model results are compared with the reported flow volumes. The model is applied to two case studies: (1) a disruption of the seaports of Los Angeles and Long Beach (LA and LB) similar to the impacts that would be felt in an earthquake; and (2) implementation of new user fees at the California ports.

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This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

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Correspondence to Linda Nozick.

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Wang, H., Nozick, L., Xu, N. et al. Modeling ocean, rail, and truck transportation flows to support policy analysis. Marit Econ Logist 20, 327–357 (2018).

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