Historical driver behavior and drive style are crucial inputs in addition to V2X connectivity data to predict future events as well as fuel consumption of the vehicle on a trip. A trip is a combination of different maneuvers a driver executes to navigate a route and interact with his/her environment including traffic, geography, topography, and weather. This study leverages big data analytics on real-world customer driving data to develop analytical modeling methodologies and algorithms to extract maneuver-based driving characteristics and generate a corresponding maneuver distribution. The distributions are further segmented by additional categories such as customer group and type of vehicle. These maneuver distributions are used to build an aggressivity distribution database which will serve as the parameter basis for further analysis with traffic simulation models. The database will also be leveraged to investigate and predict the performance of the vehicle on a trip and driver behavior based on a certain customer group, location, vehicle, and/or planned route. This big data analytics approach relieves us from traditionally heavy computing efforts, thus greatly reducing the time required for iterative modeling processes. This paper demonstrates a methodology and process to extract insights on maneuver-based driver behaviors from real-world vehicle datasets. The authors also suggest using traffic simulation models to handle the effects of conditions such as traffic and weather.