  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, Hurricane Harvey) severely disrupted the U.S. freight transport
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 four
types of natural disasters that have different risks and impacts on the
transportation network: earthquake, hurricane, tornado, and flood. The results
demonstrate the feasibility of the model and algorithmic framework to obtain
freight flows for a realistic-sized network in reasonable time (between 417 and
716 minutes). It is found that for all disaster scenarios the 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 due to
the fact that there are more states in the flood-risk areas and they are
scattered throughout the U.S.
