Though comparisons of simulation models have been conducted, few investigations have examined in detail the logical differences between models. If the output measures of effectiveness are to be interpreted correctly, it is important that the analyst understand some of the underlying logic and assumptions upon which the results are based. An understanding of model logic and its inherent effect on the results will aid the transportation analyst in the application and calibration of a simulation model. In this thesis, the car-following behavior of the CORSIM and INTEGRATION simulation models are examined in significant detail, and its impact on output results explained. In addition, the thesis presents a calibration procedure for the CORSIM sub-model, FRESIM. Currently, FRESIM is calibrated by ad hoc trial-and-error, or by utilizing empirically developed cross-referencing tables. The literature reveals that the relationship between the microscopic input parameters of the CORSIM model, and the macroscopic parameters of capacity is not understood. The thesis addresses this concern. Finally, the thesis compares the INTEGRATION and CORSIM models in freeway and urban environments. The comparison is unique in that the simulated networks were configured such that differences in results could be identified, isolated, and explained. Additionally, the simplified nature of the test networks allowed for the formulation of analytical solutions. The thesis begins by relating steady-state car-following behavior to macroscopic traffic stream models. This is done so that a calibration procedure for the FRESIM (Pipes) car-following model could be developed. The proposed calibration procedure offers an avenue to calibrate microscopic car-following behavior using macroscopic field measurements that can be easily obtained from loop detectors. The calibration procedure, while it does not overcome the inherent shortcomings of the Pipes model, does provide an opportunity to better calibrate the network FRESIM car-following sensitivity factor to existing roadway conditions. The thesis then reports an observed inconsistency in the link-specific car-following sensitivity factor of the FRESIM model. Because calibration of a network on a link-specific basis is key to an accurate network representation, a correction factor was developed that should be applied to the analytically calculated link-specific car-following sensitivity factor. The application of the correction factor resulted in observed saturation flow rates that were within 5% of the desired saturation flow rates. The thesis concludes with a comparison of the CORSIM and INTEGRATION models for transient conditions. As a result of the various intricacies and subtleties that are involved in transient behavior, the comparisons were conducted by running the models on simple networks where analytical solutions to the problem could be formulated. In urban environments, it was observed that the models are consistent in estimates of delay and travel time, and inconsistent in estimates of vehicle stops, stopped delay, fuel consumption, and emissions. Specifically, it was observed that the NETSIM model underestimates the number of vehicle stops in comparison with INTEGRATION and the analytical formulation. It was also observed that the NETSIM vehicles speed and acceleration profiles are characterized by abrupt accelerations and decelerations. These abrupt movements significantly impact stopped time delay and vehicle emissions estimates. Inconsistencies in emissions estimates can also be attributed to differences in the embedded rate tables of each model. In freeway environments for under-saturated conditions, INTEGRATION returned higher values of travel time and delay, and lower values of average speed than the FRESIM model. These results are consistent with the analytical solution, and can be attributed to the speed-flow relationship of each model. In saturated conditions, when the capacity of the bottleneck is equal to the demand volume, the emergent vehicle behavior of the FRESIM model was observed to be inconsistent with the analytical solution. The FRESIM vehicles were observed to dramatically decelerate upon entering a lower-capacity link. This deceleration behavior led to higher travel time and delay time estimates in FRESIM than in INTEGRATION. In over-saturated conditions, longer queue lengths were observed in FRESIM than in INTEGRATION, resulting in slightly higher travel and delay estimates in the FRESIM model. The reason for the discrepancy in queue lengths is unclear, as the network jam density in each model was equivalent.