Synthesizing Routes for Low Sampling Trajectories with Absorbing Markov Chains

  • Chengxuan Liao
  • Jiaheng Lu
  • Hong Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)


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.


Trajectory Point Transfer Probability Inverted List Short Path Transfer Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chengxuan Liao
    • 1
  • Jiaheng Lu
    • 1
  • Hong Chen
    • 1
  1. 1.DEKE, MOE and School of InformationRenmin University of ChinaBeijingChina

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