Synthetic Data Generation for Small-Area Demand Forecasting of Freight Flows

Author(s):

  • Paul Metaxatos1 (University of Illinois at Chicago, USA)

Abstract:
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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