Learning and inferring transportation routines

This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through an urban community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's destinati...

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Bibliographic Details
Published in:Artificial intelligence 2007-04, Vol.171 (5), p.311-331
Main Authors: Liao, Lin, Patterson, Donald J., Fox, Dieter, Kautz, Henry
Format: Article
Language:English
Subjects:
Activity recognition
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Hierarchical Markov model
Learning and adaptive systems
Location tracking
Novelty detection
Rao–Blackwellized particle filters
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Summary:This paper introduces a hierarchical Markov model that can learn and infer a user's daily movements through an urban community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user's destination and mode of transportation. To achieve efficient inference, we apply Rao–Blackwellized particle filters at multiple levels of the model hierarchy. Locations such as bus stops and parking lots, where the user frequently changes mode of transportation, are learned from GPS data logs without manual labeling of training data. We experimentally demonstrate how to accurately detect novel behavior or user errors (e.g. taking a wrong bus) by explicitly modeling activities in the context of the user's historical data. Finally, we discuss an application called “Opportunity Knocks” that employs our techniques to help cognitively-impaired people use public transportation safely.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2007.01.006