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State of the art on road traffic sensing and learning based on mobile user network log data
KTH, School of Electrical Engineering (EES), Information Science and Engineering.
KTH, School of Electrical Engineering (EES), Information Science and Engineering.ORCID iD: 0000-0002-5407-0835
2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 278, p. 110-118Article in journal (Refereed) Published
Abstract [en]

With the improvement of the storage and big data processing technology, mobile operators are able to extract and store a large amount of mobile network generated user behavior data, in order to develop various intelligent applications. One interesting application based on these data is traffic sensing, which uses techniques of learning human mobility patterns from updated location information in network interaction log data. Mobile networks, under which a huge amount of frequently updated location information of mobile users are tracked, can provide complete coverage to estimate traffic condition on roads and highways. This paper studies potential challenges and opportunities in intelligent traffic sensing from the data science point of view with mobile network generated data. Firstly, we classify the data resources available in the commercial radio network according to different taxonomy criteria. Then we outline the broken-down problems that fit in the framework of traffic sensing based on mobile user network log data. We study the existing data processing and learning algorithms on extracting traffic condition information from a large amount of mobile network log data. Finally we make suggestion on potential future work for traffic sensing on data from mobile networks. We believe the techniques and insights provided here will inspire the research community in data science to develop the machine learning models of traffic sensing on the widely collected mobile user behavior data.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 278, p. 110-118
Keywords [en]
Mobile network log, Traffic sensing, Traffic estimation, CDR, IPDR, Big data
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-223257DOI: 10.1016/j.neucom.2017.03.096ISI: 000423965000012OAI: oai:DiVA.org:kth-223257DiVA, id: diva2:1183835
Note

QC 20180219

Available from: 2018-02-19 Created: 2018-02-19 Last updated: 2018-02-19Bibliographically approved

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Huang, JinXiao, Ming

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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