Managing massive trajectories on the cloud

Abstract

With advances in location-acquisition techniques, such as GPS- embedded phones, an enormous volume of trajectory data is generated, by people, vehicles, and animals. This trajectory data is one of the most important data sources in many urban computing applications, e.g., traffic modeling, user profiling analysis, air quality inference, and resource allocation. To utilize large scale trajectory data efficiently and effectively, cloud computing platforms, e.g., Microsoft Azure, are the most convenient and economic way. However, traditional cloud computing platforms are not designed to deal with spatio-temporal data, such as trajectories. To this end, we design and implement a holistic cloud-based trajectory data management system on Microsoft Azure to bridge the gap between trajectory data and urban applications. Our system can efficiently store, index, and query large trajectory data with three functions: 1) trajectory ID-temporal query, 2) trajectory spatio-temporal query, and 3) trajectory mapmatching. The efficiency of the system is tested and tuned based on real-time trajectory data feeds. The system is currently used in many internal urban applications, as we will illustrate using case studies.