Analysing the Performance in the Identification of Relevant Places using Location Sensors on Mobile

ABSTRACT
The transportation mode such as walking, cycling or on a train
denotes an important characteristic of the mobile user’s context. In
this paper, we propose an approach to inferring a user’s mode of
transportation based on the GPS sensor on her mobile device and
knowledge of the underlying transportation network. The
transportation network information considered includes real time
bus locations, spatial rail and spatial bus stop information. We
identify and derive the relevant features related to transportation
network information to improve classification effectiveness. This
approach can achieve over 93.5% accuracy for inferring various
transportation modes including: car, bus, aboveground train,
walking, bike, and stationary. Our approach improves the accuracy
of detection by 17% in comparison with the GPS only approach,
and 9% in comparison with GPS with GIS models. The proposed
approach is the first to distinguish between motorized
transportation modes such as bus, car and aboveground train with
such high accuracy. Additionally, if a user is travelling by bus, we
provide further information about which particular bus the user is
riding. Five different inference models including Bayesian Net,
Decision Tree, Random Forest, Naïve Bayesian and Multilayer
Perceptron, are tested in the experiments. The final classification
system is deployed and available to the public.

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