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Mining Method Based on Semantic Trajectory Frequent Pattern

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Advanced Information Networking and Applications (AINA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 226))

Abstract

The current trajectory analysis mainly uses clustering method to mine public stay points from multi-user trajectories, calculate user similarity to find hotspots, extract common attributes of approximate crowds, while it’s almost no commercial value to calculate any similarity of single-user trajectories. In this paper, a method GPSTOSTFP for mining frequent patterns of individual users based on location semantics is proposed. And an improved Aporior algorithm RGA based on inverse geocoding is proposed. This kind of semantic trajectory frequent pattern mining can effectively identify and mine potential carpooling demands, provide higher precision for location-based intelligent recommendation such as sharing carpooling and HOV lane commute.

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Data and code download address:

https://github.com/TimothyLx/Mining-method-based-on-semantic-trajectory-frequent-pattern-and-carpooling-application.

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Acknowledgements

Support by the National Key R&D Program in China. Project No. 2017YFC1405403.

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Authors and Affiliations

  1. Hubei University of Technology, Wuhan, 40068, China

    Chun Liu & Xin Li

Authors
  1. Chun Liu
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  2. Xin Li
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Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Liu, C., Li, X. (2021). Mining Method Based on Semantic Trajectory Frequent Pattern. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-75075-6_12

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