An intermodal commuter network is an integration of passenger transportation systems, or modes, to a single comprehensive system that provides connections among the various modes, and improved travel choices to users. In the system examined in this dissertation, commuters access their final destination via auto, rail, and intermodal auto-to-rail modes. There are numerous highway paths by which a commuter can reach the final destination. Once on the highway, the commuter can switch to rail at stations along the rail route. The commuter may also choose to walk to the rail station closest to the trip's origin.

The main focus of this dissertation is the development of models that can estimate traffic volumes and travel costs on intermodal networks. The particular approach used in the models is demand and supply equilibrium where transportation flows are impacted by the performance of the transportation facilities. Several optimization models are formulated based on sound mathematical and economic principles, and their equilibrium conditions are derived and stated clearly. A rigorous analysis of the mathematical properties of the models proves that these conditions are satisfied from the model solutions. The objective of these models is to alleviate some of the deficiencies encountered in the urban transportation planning process.

A methodological framework is proposed which utilizes the models to analyze and evaluate operating and pricing policies in intermodal networks. The framework is designed to answer questions of interest to transportation planners, and to investigate the trade-offs between reduction in travel time and the increased cost of capacity improvements.

To link theory and practice, the models are applied, within the proposed framework, to the analysis of a real-world intermodal commuter network. Policies aimed at improving the service quality of the intermodal network are evaluated based on their benefits compared to existing conditions. The models are also used to design an optimal rail transit service by computing rail fares and headways to meet future demands.

The results of the analysis can be used by transportation planners, decision makers, transit operators, and transportation system managers to find effective ways to alleviate congestion on transportation systems. To this end, this dissertation points to areas of future research to further improve the proposed models.