Inferring High-Level Behavior from Low-Level Sensors
Abstract. We present a method of learning a Bayesian model of a traveler mov-ing through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler's current mode of transportation aswell as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. Thetraining 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 externalknowledge about bus routes and bus stops, accuracy is improved.