Predict the Next Location from Trajectory Based on Spatiotemporal Sequence

Jun Zeng, Haoran Tang, Yingbo Wu, Ling Liu, Sachio Hirokawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The achievement of wireless communication technology of mobile devices has been witnessed, which produces a large number of trajectory data of mobile users. Processing and analyzing these trajectories could obtain users' movement patterns and behavior rules, leading to provide better location-based services such as point-of-interest recommendation and location prediction. However, enormous volumes of GPS trajectory with high frequency will pose challenges in storage, transmission and computation. Recently, with the rise of social networking sites, more and more users tend to share their geographic locations in real time, thus forming check-in sequences. Hence, this paper proposes a model called SSTLP for predicting next location from trajectory based on spatiotemporal sequence. Firstly, construct location transition probability model by capturing the change of locations in historical trajectory. Secondly, compute distance possibilities of locations by the combination of normal distribution and cosine similarity and then the next location could be figured out. Experiments on real-world data set demonstrate that the proposed model outperforms traditional prediction algorithms.

Original languageEnglish
Title of host publicationProceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages109-114
Number of pages6
ISBN (Electronic)9781728126272
DOIs
Publication statusPublished - Jul 2019
Event8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019 - Toyama, Japan
Duration: Jul 7 2019Jul 11 2019

Publication series

NameProceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019

Conference

Conference8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019
CountryJapan
CityToyama
Period7/7/197/11/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Social Sciences (miscellaneous)

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  • Cite this

    Zeng, J., Tang, H., Wu, Y., Liu, L., & Hirokawa, S. (2019). Predict the Next Location from Trajectory Based on Spatiotemporal Sequence. In Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019 (pp. 109-114). [8992776] (Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2019.00032