Ubiquitous Transportation Mode Detection Using Single Cell Tower Information

Detecting the transportation mode of a user has an overwhelming significance for a vast domain of applications. It has been addressed by several systems using ubiquitous mobile phones. These systems leverage multiple cell towers' information, GPS, and inertial sensors. However, these phone sensors are limited to a small subset of phones (e.g. high-end phones or phones that support neighboring cell towers information), have high energy consumption, cannot work in certain areas (e.g. inside tunnels for GPS), and/or work only from the user side. Furthermore, there are variations in the receiving capabilities of different cell phone devices according to their brand, quality of receiver and cost which raises the problem of device heterogeneity. In this paper, a transportation mode detection system, MonoSense, is presented that leverages the phone serving cell information only. The basic idea is that the phone speed can be correlated with features extracted directly from both the serving cell tower ID and the received signal strength (RSS) from it. To achieve high detection accuracy with this limited information MonoSense leverages diversity along multiple feature dimensions to synthesize novel features. Specifically, MonoSense exploits both the time and frequency information available from the serving cell tower over different sliding window sizes. Besides, it is shown that both the logarithmic and linear RSS scales provide useful information about the movement of a phone, further enriching the feature space and leading to higher accuracy. Moreover, to overcome the heterogeneity problem, a set of device-independent features is derived and added to the feature set. MonoSense has been evaluated using 500 hours of cellular traces covering 1500 km for more than sixteen months and collected by four users using different android phones. MonoSense achieved an average precision, recall and F-measure of 87.3%, 85.9%, and 86.4%, respectively, in discriminating between stationary, walking and driving modes using only the serving cell tower information, highlighting its ability to enable a wide set of intelligent transportation applications.