In this research, we show how modeling shippers’ responses to congested freight transportation on an important segment of the Upper Mississippi River (UMR) inland navigation system strongly influences the measurement of expected economic benefits attributed to a range of congestion mitigation measures. We present a model of the UMR that integrates a shippers’ random utility model with a discrete event simulation model of the most congested 100-mile segment of the UMR system. The random utility model recognizes that waterway shippers may opt out of using the UMR in response to increased congestion and instead utilize alternative transport modes or destinations. Incorporating the dynamic response of shippers to changing operating conditions improves existing simulation models by explicitly accounting for the preferences and values of shippers, thereby providing a consistent estimate of the direct economic benefits associated with measures designed to reduce congestion and improve system performance. The major contributions of our research include demonstrating the importance of using models that capture shippers’ responses to congestion in freight transportation systems and illustrating a novel methodology for quantifying the direct economic benefits to users of measures to improve transportation on the UMR.