Transport mode detection with realistic Smartphone sensor data

We propose a novel method for automatic detection of the transport mode of a person carrying a Smart-phone. Existing approaches assume idealized positioning data with no GPS signal losses, require information from additional external sources such as real time bus locations, or only allow for a coarse distinction between very few categories (e.g. `still´, `walk´, `motorized´). Our approach is designed to deal with cluttered real-world Smartphone data and can distinguish between fine-grained transport mode categories. It is robust against GPS signal losses by including positioning data obtained from the cellular network and data from accelerometer readings. Mode detection is performed by a two-stage classification technique using randomized ensemble of classifiers combined with a Hidden Markov Model. We report promising results of an experimental performance analysis with real-world data collected by 15 volunteers during their everyday routines over a period of two months.