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Forecasting freight transportation demand with the space-time multinomial probit model

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  • Garrido, Rodrigo A.
  • Mahmassani, Hani S.

Abstract

Freight transportation demand is a highly variable process over space and time. A multinomial probit (MNP) model with spatially and temporally correlated error structure is proposed for freight demand analysis for tactical/operational planning applications. The resulting model has a large number of alternatives, and estimation is performed using Monte-Carlo simulation to evaluate the MNP likelihoods. The model is successfully applied to a data set of actual shipments served by a large truckload carrier. In addition to the substantive insights obtained from the estimation results, forecasting tests are performed to assess the model's predictive ability for operational purposes.

Suggested Citation

  • Garrido, Rodrigo A. & Mahmassani, Hani S., 2000. "Forecasting freight transportation demand with the space-time multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 403-418, June.
  • Handle: RePEc:eee:transb:v:34:y:2000:i:5:p:403-418
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    Citations

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    Cited by:

    1. Mohamed Abdel-Aty & M. Abdalla, 2004. "Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function," Transportation, Springer, vol. 31(3), pages 327-348, August.
    2. Xin Wang & Stein W. Wallace, 2016. "Stochastic scheduled service network design in the presence of a spot market for excess capacity," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(4), pages 393-413, December.
    3. Buczkowska, Sabina & de Lapparent, Matthieu, 2014. "Location choices of newly created establishments: Spatial patterns at the aggregate level," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 68-81.
    4. Mishra, Sabyasachee & Iseki, Hiroyuki & Moeckel, Rolf, 2014. "Multi entity perspective freight demand modeling technique: Varying objectives and outcomes," Transport Policy, Elsevier, vol. 35(C), pages 176-185.
    5. Bhat, Chandra R. & Guo, Jessica, 2004. "A mixed spatially correlated logit model: formulation and application to residential choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 38(2), pages 147-168, February.
    6. Liu, Yu-Hsin, 2011. "Incorporating scatter search and threshold accepting in finding maximum likelihood estimates for the multinomial probit model," European Journal of Operational Research, Elsevier, vol. 211(1), pages 130-138, May.
    7. Russo, Francesco & Musolino, Giuseppe, 2013. "Estimating demand variables of maritime container transport: An aggregate procedure for the Mediterranean area," Research in Transportation Economics, Elsevier, vol. 42(1), pages 38-49.
    8. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.
    9. Iván Sánchez-Díaz & José Holguín-Veras & Xiaokun Wang, 2016. "An exploratory analysis of spatial effects on freight trip attraction," Transportation, Springer, vol. 43(1), pages 177-196, January.
    10. Joseph Chow & Choon Yang & Amelia Regan, 2010. "State-of-the art of freight forecast modeling: lessons learned and the road ahead," Transportation, Springer, vol. 37(6), pages 1011-1030, November.
    11. Bai, Ruibin & Wallace, Stein W. & Li, Jingpeng & Chong, Alain Yee-Loong, 2014. "Stochastic service network design with rerouting," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 50-65.
    12. Zanni, Alberto M & Goulden, Murray & Ryley, Tim & Dingwall, Robert, 2017. "Improving scenario methods in infrastructure planning: A case study of long distance travel and mobility in the UK under extreme weather uncertainty and a changing climate," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 180-197.

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