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

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

References

  1. Aagaard, B.T., J.L. Blair, J. Boatwright, S.H. Garcia, R.A. Harris, A.J. Michael, et al. 2016. Earthquake outlook for the San Francisco Bay region 2014–2043, 2327–6932. Reston: US Geological Survey.

    Google Scholar 

  2. Abadi, A., P.A. Ioannou, and M.M. Dessouky. 2016. Multimodal dynamic freight load balancing. IEEE Transactions on Intelligent Transportation Systems 17 (2): 356–366.

    Article  Google Scholar 

  3. Agrawal, B., and A. Ziliaskopoulos. 2006. Shipper-carrier dynamic freight assignment model using a variational inequality approach. Transportation Research Record: Journal of the Transportation Research Board 1966: 60–70.

    Article  Google Scholar 

  4. American Association of Port Authorities. 2010. North America container traffic 1990–2009. http://aapa.files.cms-plus.com/Statistics/CONTAINER%20TRAFFIC%20NORTH%20AMERICA%201990%20-%202009.xls. Accessed 28 July 2017.

  5. Association of American Railroads. 2017a. Railroads and coal. https://www.aar.org/BackgroundPapers/Railroads%20and%20Grain.pdf. Accessed 30 July 2017.

  6. Association of American Railroads. 2017b. Railroads and grain. https://www.aar.org/BackgroundPapers/Railroads%20and%20Grain.pdf. Accessed 30 July 2017.

  7. Beuthe, M., B. Jourquin, J.-F. Geerts, and C.K. a Ndjang’Ha. 2001. Freight transportation demand elasticities: A geographic multimodal transportation network analysis. Transportation Research Part E: Logistics and Transportation Review 37 (4): 253–266.

    Article  Google Scholar 

  8. Bureau of Transportation Statistics. 2013. Pocket guide to transportation 2013. https://www.rita.dot.gov/bts/publications/pocket_guide_to_transportation/2013/system_use_and_livable_communities/table_03_05. Accessed 29 July 2017.

  9. Bureau of Transportation Statistics. 2015. Freight facts and figures. https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/TSAR_2015_final_0.pdf. Accessed 29 July 2017.

  10. Cambridge Systematics, Inc. 2007. National rail freight infrastructure capacity and investment study. https://expresslanes.codot.gov/programs/transitandrail/resource-materials-new/AARStudy.pdf. Accessed 27 July 2017.

  11. Clarke, D.B. 1995. An examination of railroad capacity and its implications for rail-highway intermodal transportation.

  12. Crainic, T.G., and L. Gilbert. 1997. Planning models for freight transportation. European Journal of Operational Research 97 (3): 409–438.

    Article  Google Scholar 

  13. Crainic, T.G., M. Florian, and J.-E. Léal. 1990. A model for the strategic planning of national freight transportation by rail. Transportation Science 24 (1): 1–24.

    Article  Google Scholar 

  14. de Cea Ch, J.N., and A. Soto. 2003. A multi-modal supply–demand equilibrium model for predicting intercity freight flows. Transportation Research Part B: Methodological 37 (7): 615–640.

    Article  Google Scholar 

  15. Dial, R.B. 2006. A path-based user-equilibrium traffic assignment algorithm that obviates path storage and enumeration. Transportation Research Part B: Methodological 40 (10): 917–936.

    Article  Google Scholar 

  16. Fan, L., W.W. Wilson, and D. Tolliver. 2010. Optimal network flows for containerized imports to the United States. Transportation Research Part E: Logistics and Transportation Review 46 (5): 735–749.

    Article  Google Scholar 

  17. Federal Highway Administration. 2016. Freight quick fact report. https://ops.fhwa.dot.gov/publications/fhwahop16083/fhwahop16083.pdf. Accessed 29 July 2017.

  18. Friesz, T.L., J.A. Gottfried, and E.K. Morlok. 1986. A sequential shipper-carrier network model for predicting freight flows. Transportation Science 20 (2): 80–91.

    Article  Google Scholar 

  19. Guelat, J., M. Florian, and T.G. Crainic. 1990. A multimode multiproduct network assignment model for strategic planning of freight flows. Transportation Science 24 (1): 25–39.

    Article  Google Scholar 

  20. Ham, H., T.J. Kim, and D. Boyce. 2005. Implementation and estimation of a combined model of interregional, multimodal commodity shipments and transportation network flows. Transportation Research Part B: Methodological 39 (1): 65–79.

    Article  Google Scholar 

  21. Harker, P.T. 1986. Alternative models of spatial competition. Operations Research 34 (3): 410–425.

    Article  Google Scholar 

  22. Harker, P.T., and T.L. Friesz. 1985. The use of equilibrium network models in logistics management: With application to the US coal industry. Transportation Research Part B: Methodological 19 (5): 457–470.

    Article  Google Scholar 

  23. Ishfaq, R. 2013. Intermodal shipments as recourse in logistics disruptions. Journal of the Operational Research Society 64 (2): 229–240.

    Article  Google Scholar 

  24. Jones, D.A., J.L. Farkas, O. Bernstein, C.E. Davis, A. Turk, M.A. Turnquist, et al. 2011. US import/export container flow modeling and disruption analysis. Research in Transportation Economics 32 (1): 3–14.

    Article  Google Scholar 

  25. Jourquin, B., and M. Beuthe. 1996. Transportation policy analysis with a geographic information system: The virtual network of freight transportation in Europe. Transportation Research Part C: Emerging Technologies 4 (6): 359–371.

    Article  Google Scholar 

  26. Jourquin, B., and S. Limbourg. 2006. Equilibrium traffic assignment on large virtual networks: Implementation issues and limits for multi-modal freight transport. European Journal of Transport and Infrastructure Research 6 (3): 205–228.

    Google Scholar 

  27. Labys, W.C., and C.W. Yang. 1997. Spatial price equilibrium as a foundation to unified spatial commodity modeling. Papers in Regional Science 76 (2): 199–228.

    Article  Google Scholar 

  28. Mahmassani, H., K. Zhang, J. Dong, C.-C. Lu, V. Arcot, and E. Miller-Hooks. 2007. Dynamic network simulation-assignment platform for multiproduct intermodal freight transportation analysis. Transportation Research Record: Journal of the Transportation Research Board 2032: 9–16.

    Article  Google Scholar 

  29. Maia, L., and A. Couto. 2013. Strategic rail network optimization model for freight transportation. Transportation Research Record: Journal of the Transportation Research Board 2378: 1–12.

    Article  Google Scholar 

  30. Miller-Hooks, E., L. Chen, R. Nair, and H. Mahmassani. 2009. Security and mobility of intermodal freight networks: Evaluation framework for simulation and assignment. Transportation Research Record: Journal of the Transportation Research Board 2137: 109–117.

    Article  Google Scholar 

  31. Nagurney, A., J. Dong, and D. Zhang. 2002. A supply chain network equilibrium model. Transportation Research Part E: Logistics and Transportation Review 38 (5): 281–303.

    Article  Google Scholar 

  32. Oak Ridge National Laboratory. 2005. Railroad network. http://cta.ornl.gov/transnet/RailRoads.html. Accessed 27 October 2017.

  33. PIERS Global Intelligence Solutions. 2006. PIERS trade data. http://www.piers.com/. Accessed 28 July 2017.

  34. Sheffi, Y. 1985. Urban transportation networks, vol. 6. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  35. Tavasszy, L., M. Minderhoud, J.-F. Perrin, and T. Notteboom. 2011. A strategic network choice model for global container flows: Specification, estimation and application. Journal of Transport Geography 19 (6): 1163–1172.

    Article  Google Scholar 

  36. The Geography of Transport Systems. 2007. Modal split at selected North American container ports. https://people.hofstra.edu/geotrans/eng/ch2en/appl2en/NA_ports_modal_split.html. Accessed 29 July 2017.

  37. Uddin, M.M., and N. Huynh. 2015. Freight traffic assignment methodology for large-scale road–rail intermodal networks. Transportation Research Record: Journal of the Transportation Research Board 2477: 50–57.

    Article  Google Scholar 

  38. Wang, H., J. Gearhart, K. Jones, C. Frazier, L. Nozick, B. Levine, and D. Jones. 2016. Estimation of an origin–destination table for US imports of waterborne containerized freight. Transportation Research Record: Journal of the Transportation Research Board 2548: 35–42.

    Article  Google Scholar 

  39. Wilson, A.G. 1970. Inter-regional commodity flows: Entropy maximizing approaches. Geographical Analysis 2 (3): 255–282.

    Article  Google Scholar 

  40. Zhang, K., R. Nair, H. Mahmassani, E. Miller-Hooks, V. Arcot, A. Kuo, et al. 2008. Application and validation of dynamic freight simulation—Assignment model to large-scale intermodal rail network: Pan-european case. Transportation Research Record: Journal of the Transportation Research Board 2066: 9–20.

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Linda Nozick.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1057/s41278-018-0108-x

Download citation

Keywords

  • Multi-model freight transportation
  • Bi-level model
  • Port disruption
  • Mode selection
  • Rail transportation
  • Logistics costs