Reference : Estimating Urban Road Traffic States Using Mobile Network Signaling Data

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/31779

Title :	Estimating Urban Road Traffic States Using Mobile Network Signaling Data
Language :	English
Author, co-author :	Derrmann, Thierry [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Frank, Raphaël [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Viti, Francesco [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Oct-2017
Journal title :	Abstract book of the 20th International Conference on Intelligent Transportation Systems
Peer reviewed :	Yes
Event name :	IEEE 20th International Conference on Intelligent Transportation Systems
Event date :	from 16-10-2017 to 19-10-2017
Abstract :	[en] It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks. Among these events, not only the initiation of calls and data sessions can be used in analyses, but also handovers between different locations that reflect mobility. In this work, we investigate if handovers can be used as a proxy metric for flows in the underlying road network, especially in urban environments. More precisely, we show that characteristic profiles of handovers within and between clusters of mobile network cells exist. We base these profiles on models from road traffic flow theory, and show that they can be used for traffic state estimation using floating-car data as ground truth. The presented model can be beneficial in areas with good mobile network coverage but low road traffic counting infrastructure, e.g. in developing countries, but also serve as an additional predictor for existing traffic state monitoring systems.
Permalink :	http://hdl.handle.net/10993/31779

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