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Discovering real-time reachable areas of a specified location is of importance for many location-based applications. The real-time reachable area of given location changes with different environments. Existing methods fail to capture real-time traffic conditions instantly. This paper provides the first attempt to discover real-time reachable areas with real-time trajectories. To address the data sparsity issue raised by the limited real-time trajectories, we propose a trajectory connection technique, which connects sub-trajectories passing the same location. Specifically, we propose a framework that combines indexing and machine learning techniques: 1) we propose a set of indexing and query processing techniques to efficiently find reachable areas with an arbitrary number of trajectory connections; 2) we propose to predict the best number of connections in any location and at any time based on multiple …