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Detecting the transportation mode of a user is important for a wide range ofapplications. While a number of recent systems addressed the transportationmode detection problem using the ubiquitous mobile phones, these studies eitherleverage GPS, the inertial sensors, and/or multiple cell towers information.However, these different phone sensors have high energy consumption, limited toa small subset of phones (e.g. high-end phones or phones that supportneighbouring cell tower information), cannot work in certain areas (e.g. insidetunnels 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 thatthe phone speed can be correlated with features extracted from both the servingcell tower ID and the received signal strength from it. To achieve highdetection accuracy with this limited information, MonoSense leverages diversityalong multiple axes to extract novel features. Specifically, MonoSense extractsfeatures from both the time and frequency domain information available from theserving cell tower over different sliding widow sizes. More importantly, weshow also that both the logarithmic and linear RSS scales can provide differentinformation about the movement of a phone, further enriching the feature spaceand leading to higher accuracy.  Evaluation of MonoSense using 135 hours of cellular traces covering 485 kmand collected by four users using different Android phones shows that it canachieve an average precision and recall of 89.26% and 89.84% respectively indifferentiating between the stationary, walking, and driving modes using onlythe serving cell tower information, highlighting MonoSense ability to enable awide set of intelligent transportation applications.

The Diversity and Scale Matter: Ubiquitous Transportation Mode Detection using Single Cell Tower Information