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Nationwide Freight Generation Models: A Spatial Regression Approach

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Abstract

This paper investigates the application of linear regression models and modeling techniques in predicting freight generation at the national level within the U.S. Specifically, the paper seeks to improve the performance and fit of linear regression models of freight generation. We provide insight into different variable transformation techniques, evaluate the use of spatial regression variables, and apply a spatial regression modeling methodology to correct for spatial autocorrelation. We conclude that the spatial regression model is the preferred specification for freight generation at the national level. The proliferation of Geographic Information Systems (GIS) within planning agencies affords more widespread use of spatial regression and our results indicate this technique would provide improvement to models that have been traditionally limited by insufficient data.

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Notes

  1. RESET adds polynomials in the OLS fitted values from the estimated model to detect general kinds of functional form misspecification (Wooldridge 2003, pg.293).

  2. The Davidson Mackinnon (1981) test involves several steps. First, the levels (untransformed) model is estimated and the fitted (predicted) values are obtained. Second, the log-transformed model is estimated and the fitted values are obtained. Third, a new variable is created equal to difference between the fitted values from the estimation of the transformed model and the log of the fitted values from the levels model (i.e., fitted values from the log-transformed model minus the log of the fitted values from the levels model). Finally, this new variable is added to the levels model and tested for statistical significance. If the levels model is correctly specified, the new variable should not be statistically significant. Thus, statistical significance suggests that the model is not correct.

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Acknowledgements

The work is this paper was partially supported by a grant from the U.S. Bureau of Transportation Statistics. A preliminary version of the paper was presented by Guo and Aultman-Hall (2005) at the 84th Annual Meeting of the Transportation Research Board in Washington DC, January 2005. The authors thank Reebie Associates for their support and for provision of the freight data without which this analysis could not have been completed.

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Authors and Affiliations

  1. School of Business Administration, The University of Vermont, 55 Colchester Avenue, Burlington, VT, 05405, USA

    David C. Novak & Christopher Hodgdon

  2. Assistant Professor of Statistics, Virginia Tech, 415B Hutcheson Hall, Blacksburg, VA, 24061, USA

    Feng Guo

  3. School of Engineering, UVM Transportation Center, The University of Vermont, 210 Colchester Avenue, Burlington, VT, 05405, USA

    Lisa Aultman-Hall

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  1. David C. Novak
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  2. Christopher Hodgdon
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  3. Feng Guo
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  4. Lisa Aultman-Hall
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Correspondence to David C. Novak.

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Novak, D.C., Hodgdon, C., Guo, F. et al. Nationwide Freight Generation Models: A Spatial Regression Approach. Netw Spat Econ 11, 23–41 (2011). https://doi.org/10.1007/s11067-008-9079-2

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