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Heterogeneous Stream Processing and Crowdsourcing for Traffic Monitoring: Highlights

  • François Schnitzler
  • Alexander Artikis
  • Matthias Weidlich
  • Ioannis Boutsis
  • Thomas Liebig
  • Nico Piatkowski
  • Christian Bockermann
  • Katharina Morik
  • Vana Kalogeraki
  • Jakub Marecek
  • Avigdor Gal
  • Shie Mannor
  • Dermot Kinane
  • Dimitrios Gunopulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)

Abstract

We give an overview of an intelligent urban traffic management system. Complex events related to congestions are detected from heterogeneous sources involving fixed sensors mounted on intersections and mobile sensors mounted on public transport vehicles. To deal with data veracity, sensor disagreements are resolved by crowdsourcing. To deal with data sparsity, a traffic model offers information in areas with low sensor coverage. We apply the system to a real-world use-case.

Keywords

smart cities crowdsourcing event pattern matching traffic stream processing big data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • François Schnitzler
    • 1
  • Alexander Artikis
    • 2
  • Matthias Weidlich
    • 3
  • Ioannis Boutsis
    • 4
  • Thomas Liebig
    • 5
  • Nico Piatkowski
    • 5
  • Christian Bockermann
    • 5
  • Katharina Morik
    • 5
  • Vana Kalogeraki
    • 4
  • Jakub Marecek
    • 6
  • Avigdor Gal
    • 1
  • Shie Mannor
    • 1
  • Dermot Kinane
    • 7
  • Dimitrios Gunopulos
    • 8
  1. 1.Technion - Israel Institute of TechnologyHaifaIsrael
  2. 2.Institute of Informatics & TelecommunicationsNCSR DemokritosAthensGreece
  3. 3.Imperial College LondonUnited Kingdom
  4. 4.Department InformaticsAthens University of Economics and BusinessGreece
  5. 5.TU Dortmund UniversityGermany
  6. 6.IBM ResearchDublinIreland
  7. 7.Dublin City CouncilIreland
  8. 8.Department of Informatics and TelecommunicationsUniversity of AthensGreece

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