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  1. Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems (MobiGIS '15)
  2. Mobility episode detection from CDR's data using switching Kalman filter
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Fusion of uncertain location data from heterogeneous sources
Geo-tagging non-spatial concepts
Scalable selective traffic congestion notification
Mobility episode detection from CDR's data using switching Kalman filter
Aggregate k-nearest neighbors queries in time-dependent road networks
An energy-conserving algorithm for the collection and reporting of data in mobile sensor networks
Validation of spatial integrity constraints in city models
$A^{2}N^{2}:$ approximate aggregate nearest neighbor queries on road networks
Distributed autonomous GIS to form teams for public safety
Towards user-centric data management: individual mobility analytics for collective services
Querying semantic trajectory episodes
Mining condensed spatial co-location patterns

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Mobility episode detection from CDR's data using switching Kalman filter

Content Provider ACM Digital Library
Author Vainikko, Eero Lind, Artjom Batrashev, Oleg Hadachi, Amnir
Abstract The detection of stay-jump-and-moving movement episodes using only cellular data is a big challenge due to the nature of the data. In this article, we propose a method to automatically detect the movement episodes (stay-jump-and-moving) from sparsely sampled spatio-temporal data, in our case Call Detail Records (CDRs), using switching Kalman filter with a new integrated movement model and cellular coverage optimization approach. The algorithm is capable of estimating the movement episodes and classifying the trajectory sequences associated to a stay, a jump or a moving action. The result of this approach can be beneficial for applications using cellular data related to traffic management, mobility profiling, and semantic enrichment.
Starting Page 63
Ending Page 69
Page Count 7
File Format PDF
ISBN 9781450339773
DOI 10.1145/2834126.2834139
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-11-03
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Call detail records Switching kalman filter
Content Type Text
Resource Type Article
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