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

Rodrigo A. Garrido; Hani S. Mahmassani

doi:10.1016/S0191-2615(99)00032-6

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

Rodrigo A. Garrido, Hani S. Mahmassani^*

^*Corresponding author for this work

Civil and Environmental Engineering

Research output: Contribution to journal › Article › peer-review

88 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 language	English (US)
Pages (from-to)	403-418
Number of pages	16
Journal	Transportation Research Part B: Methodological
Volume	34
Issue number	5
DOIs	https://doi.org/10.1016/S0191-2615(99)00032-6
State	Published - Jan 1 2000

ASJC Scopus subject areas

Transportation
Management Science and Operations Research

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10.1016/S0191-2615(99)00032-6

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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.",

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

Abstract

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