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Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information

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Personal Analytics and Privacy. An Individual and Collective Perspective (PAP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10708))

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Abstract

Automatic detection of public transport (PT) usage has important applications for intelligent transport systems. It is crucial for understanding the commuting habits of passengers at large and over longer periods of time. It also enables compilation of door-to-door trip chains, which in turn can assist public transport providers in improved optimisation of their transport networks. In addition, predictions of future trips based on past activities can be used to assist passengers with targeted information. This article documents a dataset compiled from a day of active commuting by a small group of people using different means of PT in the Helsinki region. Mobility data was collected by two means: (a) manually written details of each PT trip during the day, and (b) measurements using sensors of travellers’ mobile devices. The manual log is used to cross-check and verify the results derived from automatic measurements. The mobile client application used for our data collection provides a fully automated measurement service and implements a set of algorithms for decreasing battery consumption. The live locations of some of the public transport vehicles in the region were made available by the local transport provider and sampled with a 30-s interval. The stopping times of local trains at stations during the day were retrieved from the railway operator. The static timetable information of all the PT vehicles operating in the area is made available by the transport provider, and linked to our dataset. The challenge is to correctly detect as many manually logged trips as possible by using the automatically collected data. This paper includes an analysis of challenges due to missing or partially sampled information, and initial results from automatic recognition using a set of algorithms comparing measured trips with both live vehicle locations and static timetables. Improvement of correct recognitions is left as an ongoing challenge.

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Notes

  1. 1.

    http://www.wi-fi.org/.

  2. 2.

    https://www.bluetooth.com/.

  3. 3.

    Static timetable data is referenced from the resources of the transport provider.

  4. 4.

    https://github.com/aalto-trafficsense/public-transport-dataset.

  5. 5.

    http://cordis.europa.eu/project/rcn/102050_en.html.

  6. 6.

    http://trafficsense.aalto.fi.

  7. 7.

    http://aef.aalto.fi/en/.

  8. 8.

    AndroidTM is a trademark of Google Inc.

  9. 9.

    https://play.google.com/store/apps/details?id=fi.aalto.trafficsense.trafficsense.

  10. 10.

    https://www.hsl.fi/en.

  11. 11.

    https://www.digitransit.fi/en/developers/.

  12. 12.

    http://user47094.vs.easily.co.uk/siri/.

  13. 13.

    At the time of the study, the data was available at http://dev.hsl.fi/siriaccess/vm/json. The address has been later replaced by http://api.digitransit.fi/realtime/vehicle-positions/v1/siriaccess/vm/json.

  14. 14.

    https://developers.google.com/transit/gtfs/.

  15. 15.

    http://www.json.org/.

  16. 16.

    https://www.liikennevirasto.fi/web/en/open-data/digitraffic.

  17. 17.

    http://rata.digitraffic.fi/api/v1/history?departure_date=2016-08-26.

  18. 18.

    http://www.liikennevirasto.fi/web/en.

  19. 19.

    https://www.postgresql.org/.

  20. 20.

    https://developers.google.com/android/guides/overview.

  21. 21.

    The availability of a particular hardware sensor may vary between different device models.

  22. 22.

    http://gisgeography.com/wgs84-world-geodetic-system/.

  23. 23.

    https://developers.google.com/android/reference/com/google/android/gms/location/FusedLocationProviderApi.

  24. 24.

    Estimated by the fused location provider.

  25. 25.

    Not UNKNOWN or TILTING.

  26. 26.

    One point every 10 s from every terminal.

  27. 27.

    http://www.opentripplanner.org/.

  28. 28.

    https://www.digitransit.fi/en/developers/services-and-apis/1-routing-api/itinerary-planning/.

  29. 29.

    https://github.com/aalto-trafficsense/public-transport-dataset/tree/master/trains-json.

  30. 30.

    Bus line_name specifiers <100 can be re-used by the adjoining cities of Helsinki, Espoo and Vantaa. Bus 18 from Helsinki is included in transit_live, but the test participant used bus 18 of Espoo.

  31. 31.

    https://www.hsl.fi/sites/default/files/uploads/13_2015_raitioliikenteen_linjastosuunnitelma_netti.pdf.

  32. 32.

    http://creativecommons.org/licenses/by/4.0/.

  33. 33.

    device_data, device_data_filtered, device_models, manual_log.

  34. 34.

    commuterTrains.json, trainStations.json.

  35. 35.

    transit_live.

  36. 36.

    In the subway station entrance images offered in the repository.

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Acknowledgements

Supported by the TrafficSense project in the Aalto Energy Efficiency Programme funded by Aalto University.

In addition to authors the following persons have contributed to the TrafficSense software: Joonas Javanainen, Kimmo Karhu, Juho Saarela, Janne Suomalainen, Michailis Tziotis. In addition to the authors and software contributors the TrafficSense project has been participated by Mikko Heiskala, Jani-Pekka Jokinen, Iisakki Kosonen, Esko Nuutila, Roelant Stegmann, Markku Tinnilä, Seppo Törmä, Görkem Yetik and Indre Zliobaite.

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Authors and Affiliations

  1. Department of Computer Science, Aalto University, PO Box 15400, 00076, Aalto, Espoo, Finland

    Mikko Rinne, Mehrdad Bagheri, Tuukka Tolvanen & Jaakko Hollmén

Authors
  1. Mikko Rinne
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  2. Mehrdad Bagheri
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  3. Tuukka Tolvanen
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  4. Jaakko Hollmén
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Corresponding authors

Correspondence to Mikko Rinne or Jaakko Hollmén .

Editor information

Editors and Affiliations

  1. KDDLab, ISTI-CNR, Pisa, Italy

    Riccardo Guidotti

  2. KDDLab, University of Pisa, Pisa, Italy

    Anna Monreale

  3. KDDLab, University of Pisa, Pisa, Italy

    Dino Pedreschi

  4. Inria, École Normale Supérieure, Paris, France

    Serge Abiteboul

Licensing

Licensing

The data in the referenced github repository is licensed under the Creative Commons BY 4.0 licence Footnote 32. The data extracted from the test groupFootnote 33 is licensed by Aalto University. Train dataFootnote 34 is obtained from Digitraffic offered by the Finnish Transport Agency. The live data on public transportationFootnote 35 is compiled from a service offered by Helsinki Regional Transport. Map data from the city of HelsinkiFootnote 36 is authored by “Helsinki, kiinteistöviraston kaupunkimittausosasto”.

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Rinne, M., Bagheri, M., Tolvanen, T., Hollmén, J. (2017). Automatic Recognition of Public Transport Trips from Mobile Device Sensor Data and Transport Infrastructure Information. In: Guidotti, R., Monreale, A., Pedreschi, D., Abiteboul, S. (eds) Personal Analytics and Privacy. An Individual and Collective Perspective. PAP 2017. Lecture Notes in Computer Science(), vol 10708. Springer, Cham. https://doi.org/10.1007/978-3-319-71970-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-71970-2_8

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  • Online ISBN: 978-3-319-71970-2

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