01630295

Component

With an interdisciplinary nature of transportation studies, many transportation theories were adopted from physics, computer science, statistics, or other engineering researches. Similarly, the gravity model, originated from Newton’s law of universal gravitation, has been used in freight distribution estimation, and passenger demand analysis, for decades. Researchers have utilized transformation, optimizing parameter estimates, or adding adjustment factors to improve the gravity models. However, a zone-to-zone distance matrix, the source for impedance factor of gravity models in most practices, has many fundamental limitations. One assumption of using the distance matrix is that the friction factor is a decreasing function over a distance; i.e., the estimated shipment volume (or trips) decreases as the distance increases with a given production and attraction volume. This has been proven wrong in many cases by research and in practice. Nevertheless, the zone-to-zone distance matrix is still most-widely used in gravity models for both passenger and freight distribution estimation. The objective of this paper is to provide an alternative impedance factor of gravity model for freight distribution estimation, using a spatial correlation matrix constructed upon the correlation between the production of origin and the attraction of destination in time series. The results are compared to the gravity model using the county-to-county great circle distance, by transportation mode, with four transformation types. The U.S. domestic coal distribution data from the Energy Information Administration was used to validate the proposed approach. The case study shows that the proposed approach outperforms the conventional gravity model using a distance matrix.

This paper was sponsored by TRB committee AT015 Standing Committee on Freight Transportation Planning and Logistics.

01618707

17-06189

English

17p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Case studies; Coal; Estimation theory; Freight traffic; Gravity models; Spatial analysis; Time series analysis

Spatial correlation

United States

Freight Transportation; Operations and Traffic Management; Planning and Forecasting

Transportation Research Board Annual Meeting 2017 Paper #17-06189

TRIS, TRB, ATRI

Dec 8 2016 12:30PM