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.
Uddin, Majbah, et al. "Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty." Highlights of Sustainability, vol. 3, no. 1, Dec. 2023. https://doi.org/10.54175/hsustain3010001
Uddin, Majbah, Huynh, Nathan N., & Ahmed, Fahim (2023). Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty. Highlights of Sustainability, 3(1). https://doi.org/10.54175/hsustain3010001
Uddin, Majbah, Huynh, Nathan N., and Ahmed, Fahim, "Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty," Highlights of Sustainability 3, no. 1 (2023), https://doi.org/10.54175/hsustain3010001
@article{osti_2267631,
author = {Uddin, Majbah and Huynh, Nathan N. and Ahmed, Fahim},
title = {Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty},
annote = {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.},
doi = {10.54175/hsustain3010001},
url = {https://www.osti.gov/biblio/2267631},
journal = {Highlights of Sustainability},
issn = {ISSN 2696-628X},
number = {1},
volume = {3},
place = {United States},
publisher = {Highlights of Science},
year = {2023},
month = {12}}