Discovering Real-time Reachable Area using Trajectory Connections

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 datasets. Extensive experiments and one case study demonstrate the effectiveness and efficiency of our methods.