Transportation Mode detection Using Mobile Phones and GIS Information (original) (raw)

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 infer a user’s mode of transportation based on the GPS sensors on their mobile devices, 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. Compared with existing methods, our approach improves the accuracy of detection by 17% for GPS only approach, and 9% for GPS with GIS models. This proposed technique 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 travelling on. Five different inference models including, Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are studied in the experiments. The final classification system is deployed and available to the public.