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Analyzing large-scale human mobility data: a survey of machine learning methods and applications

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Abstract

Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

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Notes

  1. https://www.telefonica.com/en/.

  2. https://www.verizonwireless.com/featured/precision/.

  3. https://www.t-systems.com/de/en/solutions/cloud/solutions/outdoor-analytics/traffic-analysis-75652.

  4. http://www.airsage.com/.

  5. http://www.streetlightdata.com/.

  6. http://www.trendit.net/.

  7. http://www.esri.com/software/arcgis.

  8. http://euclidanalytics.com/.

  9. http://www.shoppertrak.eu/.

  10. http://retailnext.net/en/home/.

  11. http://searchengineland.com/new-google-local-search-display-230525.

  12. https://www.waze.com/en-GB/.

  13. https://www.google.co.il/maps?source=tldso.

  14. https://www.yelp.com/sf?home=1.

  15. https://itunes.apple.com/il/app/urbanspoon-restaurant-food/id284708449?mt=8.

  16. https://foursquare.com/.

  17. https://www.facebook.com/places/.

  18. https://www.uber.com/.

  19. https://www.lyft.com/.

  20. https://itunes.apple.com/il/app/find-my-friends/id466122094?mt=8.

  21. There are other paging policies that use time and spatial-based approaches for location management, e.g., as in Krishnamachari et al. [46] and Xiao et al. [94].

  22. Note the difference to trajectory modeling (Sect. 4.3), in which moving objects along a route are analyzed together.

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Acknowledgements

This work is supported by the Israeli Ministry of Science, Technology, and Space, Grant No. 3-8709: Learning and mining mobility patterns using stochastic models. We would like to thank Omer Barak and Gabriella Cohen for their help in collecting data for the survey.

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Authors and Affiliations

  1. Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel

    Eran Toch & Irad Ben-Gal

  2. Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

    Boaz Lerner & Eyal Ben-Zion

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  1. Eran Toch
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Toch, E., Lerner, B., Ben-Zion, E. et al. Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowl Inf Syst 58, 501–523 (2019). https://doi.org/10.1007/s10115-018-1186-x

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