Since the early 1990’s, public policies for transportation planning have evolved towards modally balanced transportation systems, requiring planning agencies to more precisely evaluate the capacity of their transportation systems, considering all feasible modes as well as low-cost capacity improvements. However, existing methods for capacity analysis are limited to either an individual facility or a single mode network, and thus appear insufficient for multimodal systems capacity analysis. This dissertation presents an advanced method for capacity assessment that can serve as an analytical tool in the freight transportation planning process, particularly from a multimodal perspective. The multimodal network capacity model formulated in this research takes a mathematical form of a nonlinear bi-level optimization problem with an embedded user equilibrium network assignment problem at its lower level. The bi-level formulation, referred to as the MNCP model in this dissertation, is comprehensive in the sense that many crucial factors are incorporated including multiple modes and commodities, behavioral aspects of network users, external factors, as well as the physical and operational conditions of a network. The numerical tests designed to illustrate the application of the MNCP model indicate that the algorithm developed for solving the bi-level problem has been successfully implemented. These results show the capability of the model not only to estimate the capacity of a multimodal network, but also to identify the capacity gaps over all individual facilities in the network, including intermodal facilities. By incorporating more precise capacity measures into the planning process, planning agencies would benefit from the proposed MNCP model in articulating investment priorities across all transportation modes, thus achieving their goal of developing sustainable transportation systems in a cost-effective manner.