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A Big Data Framework for Analysis of Traffic Data in Italian Highways

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Foundations of Intelligent Systems (ISMIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11177))

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Abstract

The analysis of traffic data can provide decision-makers with invaluable information. Despite the availability of methodologies specifically oriented to processing this kind of data and extract knowledge from them, few tools provide a rich set of functionalities tailored to traffic analysis in large-scale, stream-like contexts. In this paper we aim to fill this gap, by introducing an exploratory framework supporting the analysis of massive stream traffic data by either OLAP-like exploration or by resorting to advanced data mining techniques.

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Notes

  1. 1.

    Originally coined by Clive Humby in 2006, the analogy has been used since then by many others, among which Peter Sondergaard, SVP Gartner.

  2. 2.

    The Web application is based on OpenStreetMap (www.openstreetmap.org).

  3. 3.

    Data Analytics, Artificial Intelligence and Cybersecurity laboratory - http://daisy.dii.univpm.it/.

  4. 4.

    https://trap2017.poliziadistato.it/.

References

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Author information

Authors and Affiliations

  1. DII, Polytechnic University of Marche, via Brecce Bianche, 60131, Ancona, Italy

    Claudia Diamantini, Domenico Potena & Emanuele Storti

Authors
  1. Claudia Diamantini
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  2. Domenico Potena
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  3. Emanuele Storti
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Corresponding author

Correspondence to Emanuele Storti .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. American University, Washington, DC, USA

    Nathalie Japkowicz

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Jiming Liu

  4. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

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Diamantini, C., Potena, D., Storti, E. (2018). A Big Data Framework for Analysis of Traffic Data in Italian Highways. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-01851-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01850-4

  • Online ISBN: 978-3-030-01851-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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