The purpose of this project is to guide strategic investment into port capacity through the development of a policy and infrastructure evaluation model of inland waterway commodity flows. A multi-stage stochastic optimization model will be developed to evaluate tradeoffs in strategic, long-term port infrastructure investment with mid-term capacity expansion decisions and provision of complementary highway infrastructure made by public and private stakeholders, and shorter-term operational practices made by shippers and carriers. This work builds on prior MarTREC projects which developed a Multi-Commodity Assignment Problem to estimate annual commodity flows through inland waterway ports from truck Global Positioning System (GPS), marine Automatic Identification System (AIS), and the Lock Performance Management System (LPMS). The proposed project will explore critical extensions of the assignment model: 1) disaggregation of the temporal scope to reflect monthly seasonality among commodities, 2) incorporation of uncertainty related to observed vehicle and vessel movement data, and 3) inclusion of transportation costs. With these extensions the team expects to increase the accuracy and resolution of the commodity-based port throughput estimates and to allow the model to be used to not only describe the current system but to prescribe policy and project investment strategies for public and private sector transportation decision makers. Calibration and validation of the multi-stage optimization model will be done through two case studies. The regional-based study will use historical truck GPS, marine AIS, and LPMS datasets. The national-based study will use data from the Billion Ton Study led by the US Department of Energy. This will ensure a feasible and realistic base-case on which to compare future policy scenarios. This project aligns with MarTREC’s research focus area in Maritime and Multimodal Logistics Management by modeling commodity flows through ports that serve as critical connections for the multimodal freight supply chain.