• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Transport Mode Detection When Fine-grained and Coarse-grained Data Meet

    Author(s)
    Asgari, Fereshteh
    Clemencon, Stephan
    Griffith University Author(s)
    Asgari, Fereshteh
    Year published
    2018
    Metadata
    Show full item record
    Abstract
    Transport Mode Detection (TDM) algorithms in principle are developed for fine-grained data which is either high frequent accurate GPS data with/or further optional data such as accelerometer from mobile phones. The main drawback of using high frequent GPS data is the battery issue which makes it very expensive experiment to be employed for large scale data. Besides, GPS can not cover underground trajectories and some additional resource is required for such multi-modal trajectories. In this work we investigate the TDM algorithms using a combination of fine-grained (GPS) and coarse-grained (GSM) data with lower frequency ...
    View more >
    Transport Mode Detection (TDM) algorithms in principle are developed for fine-grained data which is either high frequent accurate GPS data with/or further optional data such as accelerometer from mobile phones. The main drawback of using high frequent GPS data is the battery issue which makes it very expensive experiment to be employed for large scale data. Besides, GPS can not cover underground trajectories and some additional resource is required for such multi-modal trajectories. In this work we investigate the TDM algorithms using a combination of fine-grained (GPS) and coarse-grained (GSM) data with lower frequency compared to existing studies. We first provide a comprehensive overview of transport mode detection for such data by exploring both segment based and sequence-based machine learning approaches and then we use the collected heterogeneous mobility dataset to compare different mode detection algorithms. With the obtained results, we show that TDM algorithms are still effective approach for noisy and sparse heterogeneous data. The obtained decent performance provides the opportunity of extracting precious data from a large population of users in an inexpensive approach.
    View less >
    Conference Title
    2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)
    DOI
    https://doi.org/10.1109/ICITE.2018.8492673
    Subject
    Artificial Intelligence and Image Processing
    Science & Technology
    Transportation Science & Technology
    Computer Science
    Publication URI
    http://hdl.handle.net/10072/393344
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander