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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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    Cited by:

    1. de Lima, Lilian M. & de Pelegrini Elias, Lilian & Caixeta-Filho, Jose V. & de Campos Coleti, Jamile, 2016. "Fertilizer freight rate disparity in Brazil: a regional approach," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(4), September.
    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. José Holguín-Veras & Ning Xu & Miguel Jaller & John Mitchell, 2016. "A Dynamic Spatial Price Equilibrium Model of Integrated Urban Production-Transportation Operations Considering Freight Delivery Tours," Transportation Science, INFORMS, vol. 50(2), pages 489-519, May.
    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. Karthik K. Srinivasan & Hani S. Mahmassani, 2005. "A Dynamic Kernel Logit Model for the Analysis of Longitudinal Discrete Choice Data: Properties and Computational Assessment," Transportation Science, INFORMS, vol. 39(2), pages 160-181, May.
    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. 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.
    8. Reda, Abel Kebede & Tavasszy, Lori & Gebresenbet, Girma & Ljungberg, David, 2023. "Modelling the effect of spatial determinants on freight (trip) attraction: A spatially autoregressive geographically weighted regression approach," Research in Transportation Economics, Elsevier, vol. 99(C).
    9. Galina Ševčenko-Kozlovska & Kristina Čižiūnienė, 2022. "The Impact of Economic Sustainability in the Transport Sector on GDP of Neighbouring Countries: Following the Example of the Baltic States," Sustainability, MDPI, vol. 14(6), pages 1-26, March.
    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. 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.
    13. Dhulipala, Sowjanya & Patil, Gopal R., 2020. "Freight production of agricultural commodities in India using multiple linear regression and generalized additive modelling," Transport Policy, Elsevier, vol. 97(C), pages 245-258.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Holler Branco, José Eduardo & Bartholomeu, Daniela Bacchi & Alves Junior, Paulo Nocera & Caixeta Filho, José Vicente, 2022. "Evaluation of the economic and environmental impacts from the addition of new railways to the brazilian's transportation network: An application of a network equilibrium model," Transport Policy, Elsevier, vol. 124(C), pages 61-69.
    19. Pani, Agnivesh & Sahu, Prasanta K. & Chandra, Aitichya & Sarkar, Ashoke K., 2019. "Assessing the extent of modifiable areal unit problem in modelling freight (trip) generation: Relationship between zone design and model estimation results," Journal of Transport Geography, Elsevier, vol. 80(C).
    20. Al Hajj Hassan, Lama & Mahmassani, Hani S. & Chen, Ying, 2020. "Reinforcement learning framework for freight demand forecasting to support operational planning decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    21. 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.
    22. 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.
    23. Sener, Ipek N. & Pendyala, Ram M. & Bhat, Chandra R., 2011. "Accommodating spatial correlation across choice alternatives in discrete choice models: an application to modeling residential location choice behavior," Journal of Transport Geography, Elsevier, vol. 19(2), pages 294-303.
    24. Perez-Lopez, Jose-Benito & Novales, Margarita & Orro, Alfonso, 2022. "Spatially correlated nested logit model for spatial location choice," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 1-12.
    25. Zhaoxia Guo & Weiwei Le & Youkai Wu & Wei Wang, 2019. "A Multi-Step Approach Framework for Freight Forecasting of River-Sea Direct Transport without Direct Historical Data," Sustainability, MDPI, vol. 11(15), pages 1-15, August.

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