The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the complexity of transportation systems makes it challenging for them to simulate very large geographical regions, such as multi-city metropolitan areas. In this paper, we describe the Mobiliti traffic simulator, which includes mechanisms to capture congestion delays, timing constraints, and link storage capacity constraints. The simulator is designed to support distributed memory parallel execution and be scalable on high-performance computing platforms. We introduce a method to model dynamic rerouting behavior with the addition of vehicle controller actors and reroute request events. We demonstrate the potential of the simulator by analyzing the impact of varying the population penetration rate of dynamic rerouting on the San Francisco Bay Area road network. Using high-performance parallel computing, we can simulate a day of the San Francisco Bay Area with 19 million vehicle trips with 50 percent dynamic rerouting penetration over a road network with 0.5 million nodes and 1 million links in less than three minutes. We present an analysis of system-level impacts when changing the dynamic rerouting penetration rate and examine the varying effects on different functional classes and geographical regions. Finally, we present a validation of the simulation results compared to real world data.