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

被引:0
|
作者
Metaxatos, Paul [1 ,2 ]
机构
[1] Univ Illinois, Urban Transportat Ctr, Chicago, IL 60607 USA
[2] Univ Illinois, Ctr Supply Chain Management & Logist, Chicago, IL USA
关键词
small area estimation; gravity model; freight flows;
D O I
暂无
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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-a-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.
引用
收藏
页码:42 / 51
页数:10
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