Finding Stops in Error-Prone Trajectories of Moving Objects with Time-Based Clustering
M. Zimmermann, T. Kirste, and M. Spiliopoulou. Intelligent Interactive Assistance and Mobile Multimedia Computing, volume 53 of Communications in Computer and Information Science, page 275-286. Springer Berlin Heidelberg, (2009)
Abstract
An important problem in the study of moving objects is
the identification of stops. This problem becomes more difficult due to
error-prone recording devices. We propose a method that discovers stops
in a trajectory that contains artifacts, namely movements that did not
actually take place but correspond to recording errors. Our method is
an interactive density-based clustering algorithm, for which we define
density on the basis of both the spatial and the temporal properties of a
trajectory. The interactive setting allows the user to tune the algorithm
and to study the stability of the anticipated stops.
%0 Conference Paper
%1 Zimmermann:IMC09
%A Zimmermann, Max
%A Kirste, Thomas
%A Spiliopoulou, Myra
%B Intelligent Interactive Assistance and Mobile Multimedia Computing
%D 2009
%I Springer Berlin Heidelberg
%K error finding prone stops trajectories
%P 275-286
%T Finding Stops in Error-Prone Trajectories of Moving Objects with Time-Based Clustering
%V 53
%X An important problem in the study of moving objects is
the identification of stops. This problem becomes more difficult due to
error-prone recording devices. We propose a method that discovers stops
in a trajectory that contains artifacts, namely movements that did not
actually take place but correspond to recording errors. Our method is
an interactive density-based clustering algorithm, for which we define
density on the basis of both the spatial and the temporal properties of a
trajectory. The interactive setting allows the user to tune the algorithm
and to study the stability of the anticipated stops.
%@ 978-3-642-10263-9
@inproceedings{Zimmermann:IMC09,
abstract = {An important problem in the study of moving objects is
the identification of stops. This problem becomes more difficult due to
error-prone recording devices. We propose a method that discovers stops
in a trajectory that contains artifacts, namely movements that did not
actually take place but correspond to recording errors. Our method is
an interactive density-based clustering algorithm, for which we define
density on the basis of both the spatial and the temporal properties of a
trajectory. The interactive setting allows the user to tune the algorithm
and to study the stability of the anticipated stops.},
added-at = {2014-08-20T13:37:49.000+0200},
author = {Zimmermann, Max and Kirste, Thomas and Spiliopoulou, Myra},
biburl = {https://www.bibsonomy.org/bibtex/2fdec97e90e05202d73495c1cc5452b3f/maxzi},
booktitle = {Intelligent Interactive Assistance and Mobile Multimedia Computing},
interhash = {c1edbef7d70a65a02b88b0a1fd36be23},
intrahash = {fdec97e90e05202d73495c1cc5452b3f},
isbn = {978-3-642-10263-9},
keywords = {error finding prone stops trajectories},
pages = {275-286},
publisher = {Springer Berlin Heidelberg},
series = {Communications in Computer and Information Science},
timestamp = {2014-08-20T13:37:49.000+0200},
title = {Finding Stops in Error-Prone Trajectories of Moving Objects with Time-Based Clustering},
volume = 53,
year = 2009
}