The application of intelligent transportation systems (ITS) technologies to facilitate the traffic mobility requires dynamic routing decisions. This study examines the effectiveness of Paramics, a microscopic traffic simulation model that uses a link-to-link shortest path algorithm to consider both updated link travel times and incident conditions detected through different traffic assignment techniques. This paper describes modeling of an urban highway network’s traffic conditions to investigate potential route diversion through congestion pricing strategies on toll facilities in Orlando, Florida using Paramics. The experimental design included a multi-level factorial design with three qualitative variables and four response quantitative variables. The experiment’s objective was to investigate different scenarios for reducing tolls on less congested roads (SR528 and SR417) and increasing tolls on more congested roads (SR408) to determine the impact on travelers’ route choices and overall congestion in the network. The simulation results demonstrate that the Dynamic Feedback Assignment (DFB) led to a reduction in the average queuing delay and average travel time when compared to results from the Stochastic Assignment (SA). DFB significantly affected the percentage of diversion in the network. Drivers saved 10%-16%of travel time when DFB information was provided. Results also show that per-centages of route diversion vary from one route to another and depending on the travel cost between specific origin-destination pairs. While drivers incorporate real time guidance information to maximize their own utility, not all drivers gain the same benefit. This was attributed to the limited extra capacity of the altative routes and the longer travel distance. Combining congestion pricing strategies with traffic information maximize travel time benefits.