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Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data

  • Adnan Manzoor
  • Julia S. Mollee
  • Aart T. van Halteren
  • Michel C. A. Klein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10448)

Abstract

Insufficient physical activity is a major health concern. Choosing for active transport, such as cycling and walking, can contribute to an increase in activity. Fostering a change in behavior that prefers active transport could start with automated self-monitoring of travel choices. This paper describes an experiment to validate existing algorithms for detecting significant locations, transition periods and travel modes using smartphone-based GPS data and an off-the-shelf activity tracker. A real-life pilot study was conducted to evaluate the feasibility of the approach in the daily life of young adults. A clustering algorithm is used to locate people’s important places and an analysis of the sensitivity of the different parameters used in the algorithm is provided. Our findings show that the algorithms can be used to determine whether a user travels actively or passively based on smartphone-based GPS speed data, and that a slightly higher accuracy is achieved when it is combined with activity tracker data.

Keywords

Intelligent applications Data analytics Health support systems Physical activity Clustering 

Notes

Acknowledgments

This research is supported by Philips and Technology Foundation STW, Nationaal Initiatief Hersenen en Cognitie NIHC under the partnership program Healthy Lifestyle Solutions. The authors would like to thank Lars Rouvoet and David Rip for their contribution to the data collection and their help in conducting the study.

References

  1. 1.
  2. 2.
    Sahlqvist, S., Song, Y., Ogilvie, D.: Is active travel associated with greater physical activity? The contribution of commuting and non-commuting active travel to total physical activity in adults. Prev. Med. 55, 206–211 (2012)CrossRefGoogle Scholar
  3. 3.
    Rissel, C., Curac, N., Greenaway, M., Bauman, A.: Physical activity associated with public transport use—a review and modelling of potential benefits. Int. J. Environ. Res. Public Health 9, 2454–2478 (2012)CrossRefGoogle Scholar
  4. 4.
    Saelens, B.E., Moudon, A.V., Kang, B., Hurvitz, P.M., Zhou, C.: Relation between higher physical activity and public transit use. Am. J. Public Health 104, 854–859 (2014)CrossRefGoogle Scholar
  5. 5.
    Sanders, J.P., Loveday, A., Pearson, N., Edwardson, C., Yates, T., Biddle, S.J., Esliger, D.W.: Devices for self-monitoring sedentary time or physical activity: a scoping review. J. Med. Internet Res. 18(5), e90 (2016). http://www.jmir.org/2016/5/e90/CrossRefGoogle Scholar
  6. 6.
    Klein, M.C., Manzoor, A., Middelweerd, A., Mollee, J.S., te Velde, S.J.: Encouraging physical activity via a personalized mobile system. IEEE Internet Comput. 19, 20–27 (2015)CrossRefGoogle Scholar
  7. 7.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)Google Scholar
  8. 8.
    Zhou, C., Bhatnagar, N., Shekhar, S., Terveen, L.: Mining personally important places from GPS tracks. In: 2007 IEEE 23rd International Conference on Data Engineering Workshop, pp. 517–526. IEEE (2007)Google Scholar
  9. 9.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining KDD 1996, pp. 226–231 (1996)Google Scholar
  10. 10.
    Thierry, B., Chaix, B., Kestens, Y.: Detecting activity locations from raw GPS data: a novel kernel-based algorithm. Int. J. Health Geogr. 12, 14 (2013)CrossRefGoogle Scholar
  11. 11.
    Fan, Y., Chen, Q., Liao, C.-F., Douma, F.: UbiActive: a smartphone-based tool for trip detection and travel-related physical activity assessment. In: TRB 92nd Annual Meeting Compendium of Papers, Transportation Research Board, TRB 2013 Annual Meeting (2013)Google Scholar
  12. 12.
    Bohte, W., Maat, K.: Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Transp. Res. Part C Emerg. Technol. 17, 285–297 (2009)CrossRefGoogle Scholar
  13. 13.
    Chung, E.-H., Shalaby, A.: A trip reconstruction tool for GPS-based personal travel surveys. Transp. Plan. Technol. 28, 381–401 (2005)CrossRefGoogle Scholar
  14. 14.
    Reddy, S., Mun, M., Burke, J., Estrin, D., Hansen, M., Srivastava, M.: Using mobile phones to determine transportation modes. ACM Trans. Sens. Netw. TOSN 6, 13 (2010)Google Scholar
  15. 15.
    Ellis, K., Godbole, S., Marshall, S., Lanckriet, G., Staudenmayer, J., Kerr, J.: Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms. Public Health 2, 39–46 (2014)Google Scholar
  16. 16.
    Feng, T., Timmermans, H.J.: Transportation mode recognition using GPS and accelerometer data. Transp. Res. Part C Emerg. Technol. 37, 118–130 (2013)CrossRefGoogle Scholar
  17. 17.
    Zheng, Y., Wang, L., Zhang, R., Xie, X., Ma, W.-Y.: GeoLife: managing and understanding your past life over maps. In: The Ninth International Conference on Mobile Data Management (MDM 2008), pp. 211–212. IEEE (2008)Google Scholar
  18. 18.
    Zheng, Y., Liu, L., Wang, L., Xie, X.: Learning transportation mode from raw GPS data for geographic applications on the web. In: Proceedings of the 17th International Conference on World Wide Web, pp. 247–256. ACM (2008)Google Scholar
  19. 19.
    Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec. 28, 49–60 (1999). ACMCrossRefGoogle Scholar
  20. 20.
    Schubert, E., Koos, A., Emrich, T., Züfle, A., Schmid, K.A., Zimek, A.: A framework for clustering uncertain data. Proc. VLDB Endow. 8, 1976–1979 (2015)CrossRefGoogle Scholar
  21. 21.
    Shumaker, B.P., Sinnott, R.W.: Astronomical computing: 1. Computing under the open sky. 2. Virtues of the haversine. Sky Telesc. 68, 158–159 (1984)Google Scholar
  22. 22.
    Transportation Research Board: Special Report 209 (1994)Google Scholar
  23. 23.
    Taylor, D., Mahmassani, H.: Coordinating traffic signals for bicycle progression. Transp. Res. Rec. J. Transp. Res. Board. 1705, 85–92 (2000)CrossRefGoogle Scholar
  24. 24.
    Nustats, J.Z., Geostats, J.W., Zmud, J.: Identifying the Correlates of Trip Misreporting—Results from the California Statewide Household Travel Survey GPS Study (2003)Google Scholar
  25. 25.
    Montini, L., Prost, S., Schrammel, J., Rieser-Schüssler, N., Axhausen, K.W.: Comparison of travel diaries generated from smartphone data and dedicated GPS devices. Transp. Res. Procedia 11, 227–241 (2015)CrossRefGoogle Scholar
  26. 26.
    Gong, H., Chen, C., Bialostozky, E., Lawson, C.T.: A GPS/GIS method for travel mode detection in New York City. Comput. Environ. Urban Syst. 36, 131–139 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Adnan Manzoor
    • 1
  • Julia S. Mollee
    • 1
  • Aart T. van Halteren
    • 1
  • Michel C. A. Klein
    • 1
  1. 1.Behavioural Informatics Group, Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands

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