  Detecting the transportation mode of a user is important for a wide range of
applications. While a number of recent systems addressed the transportation
mode detection problem using the ubiquitous mobile phones, these studies either
leverage GPS, the inertial sensors, and/or multiple cell towers information.
However, these different phone sensors have high energy consumption, limited to
a small subset of phones (e.g. high-end phones or phones that support
neighbouring cell tower information), cannot work in certain areas (e.g. inside
tunnels for GPS), and/or work only from the user side.
  In this paper, we present a transportation mode detection system, MonoSense,
that leverages the phone serving cell information only. The basic idea is that
the phone speed can be correlated with features extracted from both the serving
cell tower ID and the received signal strength from it. To achieve high
detection accuracy with this limited information, MonoSense leverages diversity
along multiple axes to extract novel features. Specifically, MonoSense extracts
features from both the time and frequency domain information available from the
serving cell tower over different sliding widow sizes. More importantly, we
show also that both the logarithmic and linear RSS scales can provide different
information about the movement of a phone, further enriching the feature space
and leading to higher accuracy.
  Evaluation of MonoSense using 135 hours of cellular traces covering 485 km
and collected by four users using different Android phones shows that it can
achieve an average precision and recall of 89.26% and 89.84% respectively in
differentiating between the stationary, walking, and driving modes using only
the serving cell tower information, highlighting MonoSense ability to enable a
wide set of intelligent transportation applications.
