Crowdsourced mobility prediction based on spatio-temporal contexts
Refereed conference paper presented and published in conference proceedings

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AbstractAccurate mobility prediction is becoming increasingly important in human behavior research, mainly due to many location-based applications such as mobile social networks and mobile advertisements. In this work, we propose a new crowd-sourced human mobility prediction model for public regions. We first analyze human trajectories collected through a cluster of densely deployed Wi-Fi access points (AP) in a shopping mall, and then characterize the close relationship between the human mobility patterns and the spatio-temporal contexts. Based on the distinct features of human trajectories in different types of public regions, we further propose a Markov-based crowdsourced mobility prediction method utilizing spatio-temporal contexts. We evaluate the performance of the proposed method using real traces, and show that our method is 28% more accurate in predicting human location transitions and incurs 14% smaller error in stay time prediction than the baseline methods.
All Author(s) ListPang H., Wang P., Gao L., Tang M., Huang J., Sun L.
Name of Conference2016 IEEE International Conference on Communications, ICC 2016
Start Date of Conference22/05/2016
End Date of Conference27/05/2016
Place of ConferenceKuala Lumpur
Country/Region of ConferenceMalaysia
Detailed descriptionorganized by IEEE,
LanguagesEnglish-United Kingdom

Last updated on 2021-14-10 at 23:39