Article Peer-Reviewed
Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty
1
National Transportation Research Center, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37830, USA
2
Department of Civil and Environmental Engineering, University of Nebraska-Lincoln, 2200 Vine St, 262D, Lincoln, NE 68583-0851, USA
3
South Carolina Department of Transportation, 955 Park St, Columbia, SC 29202, USA
*
For correspondence.
Academic Editor:
Received: 15 November 2023 Accepted: 21 December 2023 Published: 28 December 2023
Abstract
This paper presents a methodology for freight traffic assignment in a large-scale road-rail intermodal network under uncertainty. Network uncertainties caused by natural disasters have dramatically increased in recent years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River Flooding, and Hurricane Harvey) severely disrupted the U.S. freight transportation network, and consequently, the supply chain. To account for these network uncertainties, a stochastic freight traffic assignment model is formulated. An algorithmic framework, involving the sample average approximation and gradient projection algorithm, is proposed to solve this challenging problem. The developed methodology is tested on the U.S. intermodal network with freight flow data from the Freight Analysis Framework. The experiments consider three types of natural disasters that have different risks and impacts on transportation networks: earthquakes, hurricanes, and floods. It is found that for all disaster scenarios, freight ton-miles are higher compared to the base case without uncertainty. The increase in freight ton-miles is the highest under the flooding scenario; this is because there are more states in the flood-risk areas, and they are scattered throughout the U.S.
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Keywords
freight assignment; intermodal freight transport; road-rail intermodal; network uncertainty; stochastic programming; sample average approximation
Copyright © 2023
Uddin et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use and distribution provided that the original work is properly cited.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Cite this Article
Uddin, M., Huynh, N. N., & Ahmed, F. (2024). Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty. Highlights of Sustainability, 3(1), 1–15. https://doi.org/10.54175/hsustain3010001
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