Synthesizing routes for low sampling trajectories with absorbing Markov chains

Authors:
Chengxuan Liao;Jiaheng Lu;Hong Chen
Affiliations:
DEKE, MOE and School of Information, Renmin University of China, Beijing, China;DEKE, MOE and School of Information, Renmin University of China, Beijing, China;DEKE, MOE and School of Information, Renmin University of China, Beijing, China
Venue:
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Year:
2011

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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.