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Mobility Prediction Based on POI-Clustered Data

  • Haoyuan Chen
  • Yali Fan
  • Jing Jiang
  • Xiang Chen
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

Abstract

Predicting users’ mobility trajectories is significant for service providers, such as recommendation systems for tourist routing, emergency warning, etc. However, the former researchers predict the next location merely by observing the past individual trajectories, which usually performs poor in the accuracy of trace prediction. In this paper, POIs (Points of Interest) information is used to adjust the weight parameters of the predicted results, and the rationality and precision would be improved. The cellular towers are firstly classified into seven types of functional area through POIs. Then the target user’s next possible functional area could be speculated, which acts as a supervision of the ultimate prediction outcome. We use the DP (Dirichlet Process) mixture model to identify similarity between different users and predict users’ locations by leveraging these similar users. As is shown in the results, the methods proposed above are highly adaptive and precise when being utilized to predict users’ mobility trajectories.

Keywords

Point of interest Clustering Mobility trajectory prediction 

Notes

Acknowledgements

The work is supported in part by the NSFC (No. 61501527), Science, Technology and Innovation Commission of Shenzhen Municipality (No. JCYJ20170816151823313), Guangdong Innovative and Entrepreneurial Research Team Program (No. 2013D014), State’s Key Project of Research and Development Plan (No. 2016YFE0122900-3), the Fundamental Research Funds for the Central Universities, Guangdong Science and Technology Project (No. 2016B010126003), and 2016 Major Project of Collaborative Innovation in Guang-zhou (No. 201604046008).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Haoyuan Chen
    • 1
    • 2
  • Yali Fan
    • 1
    • 2
  • Jing Jiang
    • 1
    • 2
  • Xiang Chen
    • 1
    • 2
    • 3
  1. 1.School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.Key Lab of EDAResearch Institute of Tsinghua University in Shenzhen (RITS)ShenzhenChina
  3. 3.Starway Communications Inc.GuangzhouChina

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