This paper presents a data collection framework and its prototype application for personal activity–travel surveys through the use of smartphone sensors. The core components of the framework run on smartphones backed by cloud-based (online) services for data storage, information dissemination, and decision support. The framework employs machine-learning techniques to infer automatically activity types and travel modes with minimum interruption for the respondents. The three main components of the framework are (a) 24-h location data collection, (b) a dynamic land use database, and (c) a transportation mode identification component. The location logger is based on the smartphone network and can run for 24 h with minimal impact on smartphone battery life. The location logger is applicable equally in places where Global Positioning System signals are and are not available. The land use information is continuously updated from Internet location services such as Foursquare. The transportation mode identification module is able to distinguish six modes with 98.85% accuracy. The prototype application is conducted in the city of Toronto, Ontario, Canada, and the results clearly indicate the viability of this framework.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Using Smartphones and Sensor Technologies to Automate Collection of Travel Data


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board




    Publication date :

    2013-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Enhancing GPS-Assisted Travel Data Collection Through Smartphones

    Assemi, Behrang / Safi, Hamid / Paz, Alexander et al. | IEEE | 2020


    Using Smartphones to Collect Bicycle Travel Data in Texas

    J. G. Hudson / J. C. Duthie / Y. K. Rathod et al. | NTIS | 2012


    Tracking System to Automate Data Collection of Microscopic Pedestrian Traffic Flow

    Teknomo, Kardi / Takeyama, Yasushi / Inamura, Hajime | ArXiv | 2016

    Free access