P. Nitsche, Peter Widhalm, S. Breuss, N. Brändle, P. Maurer:
"Supporting large-scale travel surveys with smartphones - A practical approach";
Transportation Research Part C - Emerging Technologies, In Press (2013), In Press.

Collection of travel data is a key task of transportation modeling. Data collection is currently
based on costly and time-intensive questionnaires, and can thus only provide limited
cross-sectional coverage and inadequate updates. There is an urgent need for
technologically supported travel data acquisition tools. We present a novel approach for
supporting travel surveys using data collected with smartphones. Individual trips of the
person carrying the phone are automatically reconstructed and trip legs are classified into
one of eight different modes of transport. This task is performed by an ensemble of probabilistic
classifiers combined with a Discrete Hidden Markov Model (DHMM). Classification
is based on features extracted from the motion trajectory recorded by the smartphone´s
positioning system and signals of the embedded accelerometer. Our approach can cope
with GPS signal losses by including positioning data obtained from the mobile phone cell
network, and relies solely on accelerometer features when the trajectory cannot be reconstructed
with sufficient accuracy. To train and evaluate the models, 355 h of probe travel
data were collected in the metropolitan area of Vienna, Austria by 15 volunteers over a period
of 2 months. Distinguishing eight different transportation modes, the classification
results range from 65% (train, subway) to 95% (bicycle). The increasing popularity of smartphones
gives the proposed method the potential to be used on a wide-spread basis and can
complement existing travel survey methods.

Smartphones, Mobility data, Travel survey, Accelerometer, GPS, Transport modes, Mode detection

Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.