Microscopic simulation models are used in many applications for predicting pedestrian flows with high granularity. Current simulators do not allow for easy and quick switching between models. Moreover, reliable human movement data is still sparse, which is a prerequisite for model calibration and validation. These shortcomings inhibit to evaluate the capabilities of different models. This doctoral thesis develops for the first time a unified framework for the struc¬tured investigation on strengths and weaknesses of microscopic pedestrian simulation models. The empirical baseline is a highly accurate benchmark data set of 2674 human trajectories measured under real life conditions in a bidirectional corridor with a novel data collection approach using the Microsoft Kinect. The proposed simulation framework is built on a scalable and flexible system architecture to easily integrate different models. We investigate three Social Force approaches, a Cellular Automaton, the Optimal Reciprocal Collision Avoidance model and two variants of the Optimal Steps Model. A structured evaluation environment is introduced for assessing individual model capabilities to represent microscopic and macroscopic characteristics of human movement behavior. Using a simulation-based calibration procedure in our simulation framework, the parameter values for all models were estimated based on a defined set of evaluation measures. It was found that the calibration has improved the fit to the observed data set in all models. However, the grade to which individual models can be influenced by the calibration varies. The investigated models also reveal diverse capabilities concerning transferability to an independent data set. Our presented evaluation technique can easily be applied to a wider range of pedestrian modeling approaches. For future studies this will enhance the understanding of individual model characteristics and the comparison of novel modeling approaches to existing ones.