IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v45y2009i4p611-626.html
   My bibliography  Save this article

Estimating an origin-destination table for US imports of waterborne containerized freight

Author

Listed:
  • Levine, Brian
  • Nozick, Linda
  • Jones, Dean

Abstract

Containerized freight imports into the US are growing at an average of 10% per year. This traffic is concentrated at a small number of US seaports. It is therefore important to have an accurate understanding of the flow of containers from their origin country through these seaports to their final destination. This paper develops an optimization model to estimate route flows and a corresponding multi-modal origin-destination table for containers by synthesizing data on international trade and railcar movements with a gravity model for the demand of container traffic. This analysis provides insights into the balance of rail and truck inland transportation from each port.

Suggested Citation

  • Levine, Brian & Nozick, Linda & Jones, Dean, 2009. "Estimating an origin-destination table for US imports of waterborne containerized freight," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(4), pages 611-626, July.
    • Handle: RePEc:eee:transe:v:45:y:2009:i:4:p:611-626
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554508001476
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Likun Wang & Anne Goodchild & Yong Wang, 2018. "The effect of distance on cargo flows: a case study of Chinese imports and their hinterland destinations," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 20(3), pages 456-475, September.
    2. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    3. Martinez, Camil & Steven, Adams B. & Dresner, Martin, 2016. "East Coast vs. West Coast: The impact of the Panama Canal’s expansion on the routing of Asian imports into the United States," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 274-289.
    4. Kalahasthi, Lokesh & Holguín-Veras, José & Yushimito, Wilfredo F., 2022. "A freight origin-destination synthesis model with mode choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    5. Kashiha, Mona & Thill, Jean-Claude & Depken, Craig A., 2016. "Shipping route choice across geographies: Coastal vs. landlocked countries," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 1-14.
    6. Tiller, Kara Carroll & Thill, Jean-Claude, 2017. "Spatial patterns of landside trade impedance in containerized South American exports," Journal of Transport Geography, Elsevier, vol. 58(C), pages 272-285.
    7. Jones, Dean A. & Farkas, Julie L. & Bernstein, Orr & Davis, Chad E. & Turk, Adam & Turnquist, Mark A. & Nozick, Linda K. & Levine, Brian & Rawls, Carmen G. & Ostrowski, Scott D. & Sawaya, William, 2011. "U.S. import/export container flow modeling and disruption analysis," Research in Transportation Economics, Elsevier, vol. 32(1), pages 3-14.
    8. da Silva, Marcelino Aurélio Vieira & de Almeida D’Agosto, Marcio, 2013. "A model to estimate the origin–destination matrix for soybean exportation in Brazil," Journal of Transport Geography, Elsevier, vol. 26(C), pages 97-107.
    9. Huang, Dong & Grifoll, Manel & Sanchez-Espigares, Jose A. & Zheng, Pengjun & Feng, Hongxiang, 2022. "Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic," Transport Policy, Elsevier, vol. 128(C), pages 1-12.
    10. Guerrero, David, 2014. "Deep-sea hinterlands: Some empirical evidence of the spatial impact of containerization," Journal of Transport Geography, Elsevier, vol. 35(C), pages 84-94.
    11. Jin, Jiahuan & Ma, Mingyu & Jin, Huan & Cui, Tianxiang & Bai, Ruibin, 2023. "Container terminal daily gate in and gate out forecasting using machine learning methods," Transport Policy, Elsevier, vol. 132(C), pages 163-174.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:45:y:2009:i:4:p:611-626. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.