Small area statistics have become increasingly critical for the planning and management of intermodal transportation systems. However, for reasons associated with disclosure of confidential information, data is often released on a fairly coarse geography vis-à-vis a much finer geographical level. This has led to extensive research on small area estimation - i.e., estimation at a more detailed geographical level based on data at a coarser level. Most of this work has been single-area-specific or non-flow data. Freight flows, at a minimum, have origin and destination location specificity, which leads to greater complexity. This paper addresses this issue providing a methodology for small-area estimation of freight flows based on the gravity model. Preliminary empirical findings using publicly available data demonstrate the reasonableness of the method as a freight-planning tool.

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@article{oscm-2009-430,
  title={Synthetic Data Generation for Small-Area Demand Forecasting of Freight Flows},
  author={},
  journal={Operations and Supply Chain Management: An International Journal},
  year={2009},
  volume={2},
  number={1},
  pages={0--0},
  doi={http://doi.org/10.31387/oscm030121}
}

 (2009). Synthetic Data Generation for Small-Area Demand Forecasting of Freight Flows. Operations and Supply Chain Management: An International Journal, 2(1), 0-0. https://doi.org/http://doi.org/10.31387/oscm030121