• DocumentCode
    3715202
  • Title

    Encouraging Active Commuting through monitoring and analysis of commuter travel method habits

  • Author

    Salim Hasshu;Francisco Chiclana;Benjamin N. Passow;David Elizondo

  • Author_Institution
    DMUDs ITS Research Group (DIGITS), Centre of Computational Intelligence, De Montfort University Leicester, LE1 9BH, UK
  • fYear
    2015
  • Firstpage
    179
  • Lastpage
    186
  • Abstract
    The aim of this research is to understand and encourage healthier commuter travel method habits. Commuters who choose healthier travel, such as; walking, cycling or public transport methods, are known as Active Commuters (AC). However current literature suggests private car use is still the dominant method of transport. Additionally, there are very few AC monitoring and analysis applications for mobile devices, which lead to the following research question; "If commuters are able to monitor and analyse their travel habits, would this encourage them to choose AC methods?" In this work we propose a novel methodology that investigates this question. This technique was implemented and tested as an Android mobile application, giving valuable insights into AC habits. The Active Commute Tracker (ACT) mobile application was developed to include the following three components: (1) Commute Transport Method Calculation component, (2) Health component and (3) Sharing component. ACT allows users to monitor and record their commute method, distance travelled in total and commute health analysis. A basic version of this data was shared on Facebook. Users tested the application for a set number of days and provided feedback of functionality, but more importantly whether or not it encouraged AC. Feedback from users confirmed that there is a demand for an application of this nature. No user was discouraged as a direct result of ACT.
  • Keywords
    "Vehicles","Monitoring","Obesity","Mobile handsets","Legged locomotion","Androids","Humanoid robots"
  • Publisher
    ieee
  • Conference_Titel
    SAI Intelligent Systems Conference (IntelliSys), 2015
  • Type

    conf

  • DOI
    10.1109/IntelliSys.2015.7361142
  • Filename
    7361142