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

Rodrigo A. Garrido, Hani S. Mahmassani*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

89 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)403-418
Number of pages16
JournalTransportation Research Part B: Methodological
Volume34
Issue number5
DOIs
StatePublished - Jan 1 2000

ASJC Scopus subject areas

  • Transportation
  • Management Science and Operations Research

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