Assignment of Freight Traffic in a Large-Scale Intermodal Network under Uncertainty

TL;DR

This paper develops a stochastic freight traffic assignment model for large-scale intermodal networks under natural disaster uncertainties, using an algorithmic framework tested on U.S. freight data, revealing increased freight flows during disasters.

Contribution

It introduces a novel stochastic model and solution algorithm for freight assignment under uncertainty caused by natural disasters in large-scale networks.

Findings

Freight ton-miles increase under disaster scenarios.

Flooding causes the highest increase in freight flows.

Disaster impacts vary across different natural hazard types.

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.