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International Conference on Collaborative Computing: Networking, Applications and Worksharing

CollaborateCom 2017: Collaborative Computing: Networking, Applications and Worksharing pp 405–415Cite as

Using the MapReduce Approach for the Spatio-Temporal Data Analytics in Road Traffic Crowdsensing Application

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Abstract

Crowdsensing applications are becoming more popular with time. In this work, we present a crowdsensing application for capturing road traffic information to help citizens to get real-time traffic condition. Such real-time information can be beneficial for citizens to plan their journeys. However, crowdsensing in this specific case, generates spatio-temporal data collected from numerous users; storing and processing such data in real-time can be quite challenging. The MapReduce programming approach has been proposed for processing data in this context. The MapReduce jobs used to process and analyze the data captured from the crowdsensing application are presented as well as the design of the crowdsensing application. Implementation of the MapReduce jobs proposed shows that data can be effectively processed and analyzed to present near real-time information about the road traffic flow while at the same time discarding used data which is no longer required.

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Authors and Affiliations

  1. School of Innovative Technologies and Engineering, University of Technology Mauritius, La-Tour Koenig, Pointe-aux-Sables, Mauritius

    Sandhya Armoogum & Shevam Munchetty-Chendriah

Authors
  1. Sandhya Armoogum
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  2. Shevam Munchetty-Chendriah
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Corresponding author

Correspondence to Sandhya Armoogum .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Imed Romdhani

  2. Guangdong University of Petrochemical Technology, Maoming, China

    Lei Shu

  3. Osaka University, Osaka, Japan

    Hara Takahiro

  4. China University of Geosciences, Beijing, China

    Zhangbing Zhou

  5. University of Nebraska–Lincoln, Lincoln, UK

    Timothy Gordon

  6. School of Computer Science, China University of Geosciences, Wuhan, Hubei, China

    Deze Zeng

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Armoogum, S., Munchetty-Chendriah, S. (2018). Using the MapReduce Approach for the Spatio-Temporal Data Analytics in Road Traffic Crowdsensing Application. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_38

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  • DOI: https://doi.org/10.1007/978-3-030-00916-8_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00915-1

  • Online ISBN: 978-3-030-00916-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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