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Publication type: Conference paper
Type of review: Not specified
Title: Bayesian framework for mobility pattern discovery using mobile network events
Authors: Danafar, Somayeh
Piorkowski, Michal
Kryszczuk, Krzysztof
DOI: 10.21256/zhaw-4053
Proceedings: 2017 25th European Signal Processing Conference (EUSIPCO)
Pages: 1105
Pages to: 1109
Conference details: 25th European Signal Processing Conference (EUSIPCO), Kos, 28 August - 2 September 2017
Issue Date: 2017
Publisher / Ed. Institution: IEEE
ISBN: 978-0-9928626-7-1
ISSN: 2076-1465
Language: English
Subjects: Mobility; Trajectory; Prediction; Smart city
Subject (DDC): 003: Systems
Abstract: Understanding human mobility patterns is of great importance for planning urban and extra-urban spaces and communication infrastructures. The omnipresence of mobile telephony in today’s society opens new avenues of discovering the patterns of human mobility by means of analyzing cellular network data. Of particular interest is analyzing passively collected Network Events (NEs) due to their scalability. However, mobility pattern analysis based on network events is challenging because of the coarse granularity of NEs. In this paper, we propose network event-based Bayesian approaches for mobility pattern recognition and reconstruction, mode of transport recognition and modeling the frequent trajectories.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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