Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data-Reference-Cited by-同舟云学术

Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

Published:2021-05-24 Issue: Volume: Page:1-17
ISSN:0308-1060
Container-title:Transportation Planning and Technology
language:en
Short-container-title:Transportation Planning and Technology

Author:

Uddin Majbah¹^ORCID,Anowar Sabreena²³^ORCID,Eluru Naveen⁴^ORCID

Affiliation:

1. National Transportation Research Center, Oak Ridge National Laboratory, Knoxville, TN, USA

2. Department of Civil and Environmental Engineering, University of Missouri, Columbia, MI, USA

3. Department of Architectural Studies, University of Missouri, Columbia, MI, USA

4. Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, USA

Publisher

Informa UK Limited

Subject

Transportation,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/03081060.2021.1927306

Reference33 articles.

1. FREIGHT MODE CHOICE MODELS USING ARTIFICIAL NEURAL NETWORKS

2. Austin, D. 2015. “Pricing Freight Transport to Account for External Costs”. Working Paper 2015-03. Retrieved from https://EconPapers.repec.org/RePEc:cbo:wpaper:50049.

3. Biagioni, J. P., P. M. Szczurek, P. C. Nelson, and A. Mohammadian. 2009. “Tour-based Mode Choice Modeling: Using an Ensemble of (Un-) Conditional Data-Mining Classifiers.” Proceedings of the 88th Annual Meeting of the Transportation Research Board, Washington, D.C.

4. BTS. 2015. Commodity Flow Survey. Bureau of Transportation Statistics, US Department of Transportation, Washington, D.C. https://www.bts.gov/archive/publications/commodity_flow_survey/2012/united_states/index.

5. Multilayer feedforward networks for transportation mode choice analysis: An analysis and a comparison with random utility models

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