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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.
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 34 (2000)
    Issue (Month): 5 (June)
    Pages: 403-418

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    Handle: RePEc:eee:transb:v:34:y:2000:i:5:p:403-418

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    Cited by:
    1. Joseph Chow & Choon Yang & Amelia Regan, 2010. "State-of-the art of freight forecast modeling: lessons learned and the road ahead," Transportation, Springer, Springer, vol. 37(6), pages 1011-1030, November.
    2. Bai, Ruibin & Wallace, Stein W. & Li, Jingpeng & Chong, Alain Yee-Loong, 2014. "Stochastic service network design with rerouting," Transportation Research Part B: Methodological, Elsevier, Elsevier, vol. 60(C), pages 50-65.
    3. 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, Elsevier, vol. 211(1), pages 130-138, May.
    4. 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, Elsevier, vol. 38(2), pages 147-168, February.
    5. 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.
    6. 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, Springer, vol. 11(3), pages 243-272, September.
    7. 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, Springer, vol. 31(3), pages 327-348, August.


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