Traditional service area analysis has been utilizing circular buffers or road network data and travel distance to create 'reachable' areas within set threshold time. The method is limited in terms of reflecting the real world with dynamic traffic conditions. In this study the authors propose data-driven service area analysis system with real taxi GPS data, which consists of dynamic fleet service area analysis for creating service area map with real traffic condition. Taxi GPS location data, collected in Seoul for more than 2 years, is used to create data-driven service area of each vehicles in dynamic time range from 5 minutes to 30 minutes. The process is conducted on Hadoop distributed computing system due to large data size (412 GB) and computation. Proposed data-driven dynamic fleet control system would allow fleet control of multiple vehicles for wider space coverage resulting in global optimization. The suggested system can be implemented in public safety fields which is a key issue in smart cities, where safety should be maintained by implementing various methods. For example, the system can be applied to police patrol which must cover wide area simultaneously with other patrol cars to maintain safety and to respond to crime within golden time. Accurate analysis of service area within threshold time, will allow more sophisticated patrol guide, thus maximizing the efficiency. In terms of private fields, upcoming autonomous vehicles could benefit from such system. By implementing the suggested method, autonomous vehicles will be able to calculate their own service areas and will provide optimized service in terms of car-sharing and driving.