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Mensageria: A Smart City Framework for Real-Time Analysis of Traffic Data Streams

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Big Social Data and Urban Computing (BiDU 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 926))

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Abstract

Several smart city systems have focused on addressing a specific mobility problem scenario (e.g., air pollution, traffic jam) in a given city. The task of adding, extending, or porting the smart city scenario to other cities can be very challenging due to the rigid structure of such existing systems. To address this issue, in this paper we investigate common programming constructors that can be used to leverage the construction of such dynamic, smart city systems in the mobility domain. We propose Mensageria, a framework based on both the Complex Event Processing data-streaming processing paradigm and relational database management systems, which can dynamically deploy new or extend existing smart city scenarios in near real-time and maintain an updated dataset for provenance purposes. Mensageria provides several real-time primitives, such as filter, join, and enrich, that can be used to integrate, process, and analyze the city entities data streams. We discuss the generality, performance, and limitations of the proposed constructs through a real-world case study that was used in the Olympic Games of Rio in 2016 to detect, in real-time, existing and new situations that could affect the city mobility infrastructure.

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Notes

  1. 1.

    Waze – https://www.waze.com/.

  2. 2.

    Moovit – https://moovit.com/.

  3. 3.

    Twitter – https://twitter.com.

  4. 4.

    http://pt.wikipedia.org/wiki/Túnel_Rebouças.

  5. 5.

    https://pt.wikipedia.org/wiki/Linha_Vermelha_(Rio_de_Janeiro).

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Acknowledgments

The authors would like to thank Alexandre Cardeman and Dario Bizzo Marques from Centro de Operaões do Rio de Janeiro (COR).

Author information

Authors and Affiliations

  1. Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, RJ, Brazil

    Marcos Roriz Junior, Rafael Pereira de Oliveira, Felipe Carvalho, Sergio Lifschitz & Markus Endler

  2. Engenharia de Transportes – Faculdade de Ciências e Tecnologia, Universidade Federal de Goiás (UFG), Aparecida de Goiânia, GO, Brazil

    Marcos Roriz Junior

Authors
  1. Marcos Roriz Junior
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  2. Rafael Pereira de Oliveira
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  3. Felipe Carvalho
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  4. Sergio Lifschitz
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  5. Markus Endler
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Corresponding author

Correspondence to Sergio Lifschitz .

Editor information

Editors and Affiliations

  1. Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil

    Jonice Oliveira

  2. Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Claudio M. Farias

  3. Inria/CNRS, University of Montpellier, Montpellier, France

    Esther Pacitti

  4. University of Calabria (Unical), Rende (CS), Italy

    Giancarlo Fortino

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Roriz Junior, M., de Oliveira, R.P., Carvalho, F., Lifschitz, S., Endler, M. (2019). Mensageria: A Smart City Framework for Real-Time Analysis of Traffic Data Streams. In: Oliveira, J., Farias, C., Pacitti, E., Fortino, G. (eds) Big Social Data and Urban Computing. BiDU 2018. Communications in Computer and Information Science, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-11238-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-11238-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11237-0

  • Online ISBN: 978-3-030-11238-7

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