Probabilistic Modeling of Mobile Agents' Trajectories

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00177699" target="_blank" >RIV/68407700:21230/10:00177699 - isvavai.cz</a>
Result on the web
<a href="http://www.springerlink.com/content/978-3-642-15419-5#section=759276&page=1&locus=0" target="_blank" >http://www.springerlink.com/content/978-3-642-15419-5#section=759276&page=1&locus=0</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-15420-1_6" target="_blank" >10.1007/978-3-642-15420-1_6</a>

Result language
angličtina
Original language name
Probabilistic Modeling of Mobile Agents' Trajectories
Original language description
We present a method for learning characteristic motion patterns of mobile agents. The method works on two levels. On the first level, it uses the expectation-maximization algorithm to build a Gaussian mixture model of the spatial density of agents' movement. On the second level, agents' trajectories as expressed as sequences of the components of the mixture model; the sequences are subsequently used to train hidden Markov models. The trained hidden Markov models are then employed to determine agent type, predict further agent movement or detect anomalous agents. The method has been evaluated in the maritime domain using ship trajectory data generated by the AgentC maritime traffic simulation.
Czech name
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Czech description
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Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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