Real-time streaming mobility analytics

    In our research we tested distributed streaming algorithms
    and infrastructures to process large scale mobility data for fast
    reaction time prediction. We use the D4D Challenge data set as a source
    to generate, by multiplying with noise, even larger realistic data

    Instead of addressing the problem of identifying the exact location
    and movement of an individual user (that the data set is not sufficiently
    detailed for), we learn global patterns both on the user level (home,
    work location, daily routes) and the traffic (typical routes at time
    of the day).

    As a key performance indicator of our applications, we measure the running time and
    the error of predictions in short range (5 minutes to 1 hour) and long
    range (daily, weekly) of the location of an individual user and the
    density in a given area. Over a cluster of a few old dual core servers, we are
    capable of processing tens of thousands of record in a second. Our results open
    the possibility for efficient real time mobility predictions of even
    large metropolitan areas as well.

    We demonstrate our solution via a fast reaction visual dashboard application
    that can form the base of emergency or rescue services as well as
    provide grounds for ride sharing, traffic planning and optimization,
    thus saving natural resources.

    Andras Garzo, Istvan Petras, Csaba Istvan Sidlo, Andras A. Benczur
    NetMob 2013 - Third conference on the Analysis of Mobile Phone Datasets. May 1-3, 2013, MIT, Boston, USA