• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1636-1645
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• 2016

In navigation systems that use collected trajectory for routing, the number and diversity of trajectory data are crucial despite the infeasible limitation which is that all routes should be collected in person. This paper suggests an algorithm combining trajectories only by collected GPS data and generating new routes for solving this problem. Using distance between two trajectories, the algorithm estimates road intersection, in which it also predicts the correlated direction of them with geographical coordinates and makes a decision to combine them by the correlated direction. With combined and generated trajectory data, this combination way allows trajectory-based navigation to guide more and better routes. In our study, this solution has been introduced. However, the ways in which correlated direction is decided and post-process works have been revised to use the sequential pattern of triangles' area GPS information between two trajectories makes in road intersection and intersection among sets comprised of GPS points. This, as a result, reduces unnecessary combinations resulting redundant outputs and enhances the accuracy of estimating correlated direction than before.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.386-395
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• 2016

In trajectory-based navigation systems, a huge amount of trajectory data is needed for efficient route explorations. However, it would be very hard to collect trajectories from all the possible start and destination combinations. To provide a practical solution to this problem, we suggest a method combining collected GPS trajectories data into additional generated trajectories with new start and destination combinations without road information. We present a trajectory combination algorithm and its implementation with Scala programming language on Spark platform for big data processing. The experimental results proved that the proposed method can effectively populate the collected trajectories into valid trajectory paths more than three hundred times.