Recent years have witnessed significant efforts in developing and evaluating vehicle-based passive and active safety systems to reduce traffic accidents. In addition, there is growing interest in the use of microscopic simulation models for evaluating operational strategies. Both activities require quantitative characterization of driver behavior in real-world situations. Historically, such characterizations have been difficult to obtain, but the data available from large-scale naturalistic driving studies (NDS) have the potential to change this situation. However, identifying relevant events from an NDS database and reducing the NDS data to estimate relevant features of the events are still something of a challenge. This study used freeway brake-to-stop events on congested freeways as examples to describe methods for identifying relevant events. It then estimated event features, such as initial speeds for leading and following vehicles, reaction times for leading and following drivers, and changes in the drivers' braking rates. A suitably representative sample of such estimates could be used to support evaluation of vehicle-based safety countermeasures or provide inputs to traffic simulation models.