Understanding the mobility of a traveller from mobile sensor data is an important area of work in context aware and ubiquitous computing. Given a multimodal GPS trace, we will identify where in the GPS trace the traveller changed transportation modes. For example, where in the GPS trace the traveller alight a bus and boards a train, or where did the client stop running and start walking. Using data mining schemes to understand mobility data, in conjunction with real world observations, we propose an algorithm to identify mobility transfer points automatically. We compared the proposed algorithm against the state of the art that is used in the previously proposed work. Evaluation on real world data collected from GPS enabled mobile phones indicate that the proposed algorithm is accurate, has a good coverage, and a good asymptotic run time complexity.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Automated transportation transfer detection using GPS enabled smartphones


    Beteiligte:
    Stenneth, Leon (Autor:in) / Thompson, Kenville (Autor:in) / Stone, Waldin (Autor:in) / Alowibdi, Jalal (Autor:in)


    Erscheinungsdatum :

    01.09.2012


    Format / Umfang :

    947258 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Transportation Mode Detection by Using Smartphones and Smartwatches with Machine Learning

    Hasan, Raed Abdullah / Irshaid, Hafez / Alhomaidat, Fadi et al. | Springer Verlag | 2022




    Detecting the transportation mode for context-aware systems using smartphones

    Quintella, Carlos Alvaro de M. S. / Andrade, Leila C. V. / Campos, Carlos Alberto V. | IEEE | 2016


    Monitoring Air Quality Using an IoT-Enabled Air Pollution System on Smartphones

    Shamsuddin, Shareen Adlina / Awal, Wahyu Ramadhan Nurudin / Dahalan, Mohd Rohaimi Mohd et al. | Springer Verlag | 2022