Abstract
We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.
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Patterson, D.J., Liao, L., Fox, D., Kautz, H. (2003). Inferring High-Level Behavior from Low-Level Sensors. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds) UbiComp 2003: Ubiquitous Computing. UbiComp 2003. Lecture Notes in Computer Science, vol 2864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39653-6_6
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DOI: https://doi.org/10.1007/978-3-540-39653-6_6
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