Networks and Spatial Economics

, Volume 11, Issue 1, pp 23–41 | Cite as

Nationwide Freight Generation Models: A Spatial Regression Approach

  • David C. Novak
  • Christopher Hodgdon
  • Feng Guo
  • Lisa Aultman-Hall


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.


Freight generation Linear Models National freight planning Spatial analysis Freight commodity generation 



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.


  1. Agyemang Duah K, Hall F (1997) Spatial transferability of an ordered response model for trip generation. Transportation Research Part A 31a(5):389–402Google Scholar
  2. Al-Deek HM (2001) Comparison of two approaches for modeling freight movement at seaports. Journal of Computing in Civil Engineering (October), 284–291. doi:10.1061/(ASCE)0887-3801(2001)15:4(284)Google Scholar
  3. Al-Deek HM, Johnson G, Mohamed A, El-Maghraby A (2000) Truck trip generation models for seaports with container and trailer operation. Transportation Research Record 1719:1–9. doi: 10.3141/1719-01 CrossRefGoogle Scholar
  4. Aultman-Hall L, Johnson B, Aldridge B (2000) Assessing potential for modal substitution from statewide freight commodity flow data. Transportation Research Record 1719:10–16. doi: 10.3141/1719-02 CrossRefGoogle Scholar
  5. Bell MGH (2000) A game theory approach to measuring the performance reliability of transportation networks. Transportation Research Part B: Methodological 34:533–545. doi: 10.1016/S0191-2615(99)00042-9 CrossRefGoogle Scholar
  6. Bell MGH, Iida Y (1997) Transportation network analysis. Wiley, New YorkGoogle Scholar
  7. Bhat C, Zhao H (2002) The spatial analysis of activity stop generation. Transportation Research Part B 36:557–575. doi: 10.1016/S0191-2615(01)00019-4 CrossRefGoogle Scholar
  8. Cambridge Systematics Inc. Leeper CC I, Sydec Inc., Thomas M, Corsi, and Curtis M. Grimm. (1997). “A Guidebook for Forecasting Freight Transportation Demand.” NCHRP 388, Transportation Research Board, Washington D.C.Google Scholar
  9. Chatterjee A (2004) Freight transportation planning for urban areas. ITE Journal 74(12):20–24Google Scholar
  10. Chen A, Yang H, Hong KL, Tang WH (2002) Capacity reliability of a road network: an assessment methodology and numerical results. Transportation Research B: Methodological 36:225–252CrossRefGoogle Scholar
  11. Cook RD, Weisberg S (1983) Diagnostics for heteroskedasticity in regression. Biometrika 70:1–10. doi: 10.1093/biomet/70.1.1 CrossRefGoogle Scholar
  12. Crainic TG (2002) Long haul freight transportation. Handbook of transportation science, chapter 13, 2nd edn. Kluwer, Dordrecht, pp 451–515Google Scholar
  13. Davidson R, MacKinnon JG (1981) Several tests for model specification in the presence of alternative hypotheses. Econometrica 49:781–793. doi: 10.2307/1911522 CrossRefGoogle Scholar
  14. De Jong G, Gunn H, Walker W (2004) National and international freight models: an overview and ideas for future development. Transport Reviews 24(1):103–124Google Scholar
  15. Eatough C, Brich J, Stephen C, Demestsky MJ (2000) A statewide intermodal freight transportation planning methodology. Journal of Transportation Research Forum 39(1):145–155Google Scholar
  16. Feitelson E, Salomon I (2000) The implications of differential network flexibility for spatial structures. Transportation Research Part A: Policy and Practice 34:459–479. doi: 10.1016/S0965-8564(99)00028-2 CrossRefGoogle Scholar
  17. Fisher M, Ang-Olson J, La A (2000) External urban truck trips based on commodity flows: a model. Transportation Research Record 1707:73–80. doi: 10.3141/1707-09 CrossRefGoogle Scholar
  18. Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography. SAGE, LondonGoogle Scholar
  19. Garrido RA, Mahmassani HS (2000) Forecasting freight transportation demand with the space–time multinomial probit model. Transportation Research Part B 34:403–418. doi: 10.1016/S0191-2615(99)00032-6 CrossRefGoogle Scholar
  20. Giuliano G, Gordon P, Pan, Qisheng, Park, J., Wang, L. (2007) Estimating freight flows for metropolitan area highway networks using secondary data sources. Networks and Spatial Economics (in press). DOI 10.1007/s11067-007-9024-9. [online]
  21. Guo F, Aultman-Hall L (2005) Alternative nationwide freight generation models. Proceedings of the Transportation Research Board 84th Annual Meeting, Washington DC, January 2005.Google Scholar
  22. Holguín-Veras J (2001) An assessment of methodological alternatives for a regional freight model. Appendix I: literature review on freight transportation demand modeling. New York Metropolitan Transportation Council Report. New York, NY. [online]
  23. Holguín-Veras J, Patil GI (2007) A multicommodity integrated freight origin–destination synthesis model. Networks and Spatial Economics (in press). doi 10.1007/s11067-007-9053-4 [online]
  24. Holguín-Veras J, Lopez-Genao Y, Salam A (2002) Truck-trip generation at container terminals: results from a nationwide survey. Transportation Research Record, 1790:89–96CrossRefGoogle Scholar
  25. Huang WJ, Smith RL Jr. (1999) Using commodity flow survey data to develop a truck travel-demand model for Wisconsin. Transportation Research Record 1685:1–6. doi: 10.3141/1685-01 CrossRefGoogle Scholar
  26. Miller HJ (1999) Potential contributions of spatial analysis to Geographic Information Systems for Transportation (GIS-T). Geographical Analysis 31:373–399CrossRefGoogle Scholar
  27. Morlok EK, Chang DJ (2004) Measuring capacity flexibility of a transportation system. Transportation Research Part A: Policy and Practice 38:405–420. doi: 10.1016/j.tra.2004.03.001 CrossRefGoogle Scholar
  28. Nijkamp P, Reggiani A, Tsang WF (2004) Comparative modelling of interregional transport flows. European Journal of Operational Research 155(3):584–602. doi: 10.1016/j.ejor.2003.08.007 CrossRefGoogle Scholar
  29. Ortuzar JdD, Willumsen LG (2001) Modeling transport, 3rd edn. Wiley, West SussexGoogle Scholar
  30. Pendyala RM, Shankar VN, McCullough RG (2000) Freight travel demand modeling. Synthesis of approaches and development of a framework. Transportation Research Record 1725:9–18. doi: 10.3141/1725-02 CrossRefGoogle Scholar
  31. Ramsey JB (1969) Tests for specification errors in classical least-squares regression analysis. Journal of the Royal Statistical Society: Series B 31:350–371Google Scholar
  32. Regan AC, Garrido R (2001) Freight demand and shipper behavior modeling: state-of-the-art, directions for the future. In: Hensher DA, King J (eds) The leading edge of travel behavior research. Pergamon, New YorkGoogle Scholar
  33. Scott DM, Novak DC, Aultman-Hall L, Guo F (2006) Network robustness index: a new method for identifying critical links and evaluating the performance of transportation networks. Journal of Transport Geography 14:215–227. doi: 10.1016/j.jtrangeo.2005.10.003 CrossRefGoogle Scholar
  34. Sorratini JA, Smith RL Jr. (2000) Development of a statewide truck trip forecasting model based on commodity flows and input–output coefficients. Transportation Research Record 1707:49–56. doi: 10.3141/1707-06 CrossRefGoogle Scholar
  35. Southworth F (2003) Freight transportation planning: models and methods. Transportation Systems Planning, Konstadinos, CRC, New YorkGoogle Scholar
  36. Tadi RR, Balbach P (1994) Truck trip generation characteristics of nonresidential land uses. ITE Journal 64(7):44–47Google Scholar
  37. U.S. Army Corps of Engineers (USACE). (2005). A survey of the freight transportation demand literature and a comparison of elasticity estimates. January 1, 2005. The Navigation Economic Technologies Program, IWR Report 05-NETS-R-01. [online]
  38. U.S. Department of Commerce, Census Bureau, and U.S. Department of Transportation, Bureau of Transportation Statistics. (2002). Commodity Flow Survey (CFS), December 2006. [online]
  39. Wigan MR, Southworth F (2006) What’s wrong with freight models, and what should we do about it? Proceedings from the 85th Annual Meeting of the Transportation Research Board, CD-ROM.Google Scholar
  40. Wisetjindawat W, Sano K, Matsumoto S (2006) Commodity distribution model incorporating spatial interactions for urban freight movement. Transportation Research Record 1966:41–50. doi: 10.3141/1966-06 CrossRefGoogle Scholar
  41. Wooldridge JM (2003) Introductory econometrics. A modern approach. Thomson/South-Western, MasonGoogle Scholar
  42. Zhang P, Peeta S, Friesz T (2005) Dynamic game theoretic model of multi-layer infrastructure networks. Networks and Spatial Economics 5:147–178. doi: 10.1007/s11067-005-2627-0 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • David C. Novak
    • 1
  • Christopher Hodgdon
    • 1
  • Feng Guo
    • 2
  • Lisa Aultman-Hall
    • 3
  1. 1.School of Business AdministrationThe University of VermontBurlingtonUSA
  2. 2.Assistant Professor of StatisticsVirginia TechBlacksburgUSA
  3. 3.School of Engineering, UVM Transportation CenterThe University of VermontBurlingtonUSA

Personalised recommendations