@inbook {IOPORT.05973263,
    author       = {Liao, Chengxuan and Lu, Jiaheng and Chen, Hong},
    title        = {Synthesizing routes for low sampling trajectories with absorbing Markov chains.},
    year         = {2011},
    booktitle    = {Web-age information management. 12th international conference, WAIM 2011, Wuhan, China, September 14--16, 2011. Proceedings},
    isbn         = {978-3-642-23534-4},
    pages        = {614-626},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-23535-1_52},
    abstract     = {Summary: 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.},
    identifier   = {05973263},
}