Abstract
The trajectory research has been an attractive and challenging topic which blooms various interesting location based services. How to synthesize routes by utilizing the previous users’ GPS trajectories is a critical problem. Unfortunately, most existing approaches focus on only spatial factors and deal with high sampling GPS data, but low-sampling trajectories are very common in real application scenarios. This paper studies a new solution to synthesize routes between locations by utilizing the knowledge of previous users’ low-sampling trajectories to fulfill their spatial queries’ needs. We provide a thorough treatment on this problem from complexity to algorithms. (1) We propose a shared-nearest-neighbor (SNN) density based algorithm to retrieve a transfer network, which simplifies the problem and shows all possible movements of users. (2) We introduce three algorithms to synthesize route: an inverted-list baseline algorithm, a turning-edge maximum probability product algorithm and a hub node transferring algorithm using an Absorbing Markov Chain model. (3) By using real-life data, we experimentally verify the effectiveness and the efficiency of our three algorithms.
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Liao, C., Lu, J., Chen, H. (2011). Synthesizing Routes for Low Sampling Trajectories with Absorbing Markov Chains. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23535-1_52
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DOI: https://doi.org/10.1007/978-3-642-23535-1_52
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