Advanced modeling tools and methods are essential components for the analyses of congested conditions and advanced Intelligent Transportation Systems (ITS) strategies such as Managed Lanes (ML). A number of tools with different analysis resolution levels have been used to assess these strategies. These tools can be classified as sketch planning, macroscopic simulation, mesoscopic simulation, microscopic simulation, static traffic assignment, and dynamic traffic assignment tools. Due to the complexity of the managed lane modeling process, this dissertation investigated a Multi-Resolution Modeling (MRM) approach that combines a number of these tools for more efficient and accurate assessment of ML deployments. This study clearly demonstrated the differences in the accuracy of the results produced by the traffic flow models incorporated into different tools when compared with real-world measurements. This difference in the accuracy highlighted the importance of the selection of the appropriate analysis levels and tools that can better estimate ML and General Purpose Lanes (GPL) performance. The results also showed the importance of calibrating traffic flow model parameters, demand matrices, and assignment parameters based on real-world measurements to ensure accurate forecasts of real-world traffic conditions. In addition, the results indicated that the real-world utilization of ML by travelers can be best predicated with the use of dynamic traffic assignment modeling that incorporates travel time, toll, and travel time reliability of alternative paths in the assignment objective function. The replication of the specific dynamic pricing algorithm used in the real-world in the modeling process was also found to provide the better forecast of ML utilization. With regards to Connected Vehicle (CV) operations on ML, this study demonstrated the benefits of using results from tools with different modeling resolution to support each other’s analyses. In general, the results showed that providing toll incentives for Cooperative Adaptive Cruise Control (CACC)-equipped vehicles to use ML is not beneficial at lower market penetrations of CACC due to the small increase in capacity with these market penetrations. However, such incentives were found to be beneficial at higher market penetrations, particularly with higher demand levels.