Trajectory-Based User Encounter Prediction Over Wireless Sensor Networks
People or friends may encounter with each other offline, when they have a location proximity. With the rapid development of the wireless sensor network, smart city applications can leverage the sensed data of people’s mobility or trajectory to predict their future encounter opportunity and then arrange their offline activities (e.g., meeting, travel) accordingly. This paper studies the encounter prediction problem of mobile users by mining the similarity between their sensed mobile trajectories. We define the similarity of two mobile trajectories both temporally and spatially, and then propose two approaches, namely a probabilistic similarity maximization algorithm and a machine leaning based prediction algorithm, for addressing the encounter prediction problem. Results over a real-world social network dataset show that the proposed recurrent neural network based model can predict the encounter of two users precisely, and it outperforms the probabilistic algorithm and other algorithms, in terms of the precision and F1 score.
KeywordsMobility pattern Sensed trajectory Recurrent neural network
We would like to thank Ledongli Co. Ltd., for providing the trajectory data used to support the findings of the study under license. The trajectory data used to support the findings of the study were supplied by Ledongli Co. Ltd under license and so cannot be made freely available. Requests for access to these data should be made to Mr. Yuanyuan Zhang. [Contact: firstname.lastname@example.org] This work was supported by Shenzhen Key Fundamental Research Projects JCYJ20160330095313861 and by NSFC under Grants 61572051, 61632017.
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