Title : Learning and Inferring Transportation Routines
Author : Lin Liao, Donald J. Patterson, Dieter Fox, Henry Kautz
Abstract :
This paper introduces a hierarchical Markov model that can learn and infer auser’s daily movements through an urban community. The model uses multiple levelsof abstraction in order to bridge the gap between raw GPS sensor measurementsand high level information such as a user’s destination and mode of transportation.To achieve efficient inference, we apply Rao-Blackwellized particle filters at multiplelevels of the model hierarchy. Locations such as bus stops and parking lots, wherethe user frequently changes mode of transportation, are learned from GPS datalogs without manual labeling of training data. We experimentally demonstrate howto accurately detect novel behavior or user errors (e.g. taking a wrong bus) byexplicitly modeling activities in the context of the user’s historical data. Finally, wediscuss an application called “Opportunity Knocks” that employs our techniques tohelp cognitively-impaired people use public transportation safely.
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