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Traffic Data: Exploratory Data Analysis with Apache Accumulo

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Traffic Mining Applied to Police Activities (TRAP 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 728))

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Abstract

The amount of traffic data collected by automatic number plate reading systems constantly incrseases. It is therefore important, for law enforcement agencies, to find convenient techniques and tools to analyze such data. In this paper we propose a scalable and fully automated procedure leveraging the Apache Accumulo technology that allows an effective importing and processing of traffic data. We discuss preliminary results obtained by using our application for the analysis of a dataset containing real traffic data provided by the Italian National Police. We believe the results described here can pave the way to further interesting research on the matter.

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Notes

  1. 1.

    http://accumulo.apache.org.

  2. 2.

    http://hadoop.apache.org.

  3. 3.

    https://zookeeper.apache.org.

  4. 4.

    http://thrift.apache.org.

  5. 5.

    Other Bigtable systems have integrated server-side programming, e.g. Apache HBase provides coprocessors, but they are not so deeply and nicely integrated in the underlying technology as iterators [3].

References

  1. Chang, Fay, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, D.A. Wallach, M. Burrows, T. Chandra, A. Fikes, and R.E. Gruber. 2008. Bigtable: A distributed storage system for structured data. ACM Transactions on Computer Systems 26 (2): 4:1–4:26.

    Article  Google Scholar 

  2. Chen, Min, Shiwen Mao, and Yunhao Liu. 2014. Big data: A survey. Mobile Networks and Applications 19 (2): 171–209.

    Article  Google Scholar 

  3. Cordova, Aaron, Billie Rinaldi, and Michale Wall. 2015. Accumulo: Application development, table design, and best practices, 1st ed. Beijing: O’Reilly Media, Inc.

    Google Scholar 

  4. Elfeky, Mohamed G., Walid G. Aref, and Ahmed K. Elmagarmid. 2005. Periodicity detection in time series databases. IEEE Transactions on Knowledge and Data Engineering 17 (7): 875–887.

    Article  Google Scholar 

  5. Erl, Thomas, Wajid Khattak, and Paul Buhler. 2016. Big data fundamentals: Concepts, drivers & techniques. Boston: Prentice Hall Press.

    Google Scholar 

  6. Hoh, Baik, Marco Gruteser, Ryan Herring, Jeff Ban, Daniel Work, Juan-Carlos Herrera, Alexander M. Bayen, Murali Annavaram, and Quinn Jacobson. 2008. Virtual trip lines for distributed privacy-preserving traffic monitoring. In Proceedings of the 6th international conference on Mobile Systems, Applications, and Services, MobiSys ’08, 15–28. USA: ACM.

    Google Scholar 

  7. Keller, Richard, M., Shubha Ranjan, Mei, Y. Wei, and Michelle, M. Eshow. 2016. Semantic representation and scale-up of integrated air traffic management data. Proceedings of the International Workshop on Semantic Big Data SBD ’16, 4:1–4:6. USA: ACM.

    Google Scholar 

  8. Kepner, J., W. Arcand, D. Bestor, B. Bergeron, C. Byun, V. Gadepally, M. Hubbell, P. Michaleas, J. Mullen, A. Prout, A. Reuther, A. Rosa, and C. Yee. 2014. Achieving 100,000,000 database inserts per second using accumulo and d4m. In 2014 IEEE High Performance Extreme Computing Conference (HPEC), 1–6.

    Google Scholar 

  9. Knospe, W., L. Santen, A. Schadschneider, and M. Schreckenberg. 2002. Single-vehicle data of highway traffic: Microscopic description of traffic phases. Physical Review Series E 65: 056133.

    Article  MATH  Google Scholar 

  10. Koller, D., J. Weber, T. Huang, J. Malik, G. Ogasawara, B. Rao, and S. Russell. 1994. Towards robust automatic traffic scene analysis in real-time. In Proceedings of the 33rd IEEE Conference on Decision and Control, 4, 3776–3781.

    Google Scholar 

  11. Necula, Emilian. 2015. Analyzing traffic patterns on street segments based on gps data using R. In 18th Euro Working Group on Transportation, EWGT, 10, 276–285. The Netherlands: Transportation Research Procedia.

    Google Scholar 

  12. Patil, Swapnil, Milo Polte, Kai Ren, Wittawat Tantisiriroj, Lin Xiao, J. López, Garth Gibson, Adam Fuchs, and Billie Rinaldi. 2011. Ycsb++: benchmarking and performance debugging advanced features in scalable table stores. In Proceedings of the 2nd ACM Symposium on Cloud Computing, 9. ACM.

    Google Scholar 

  13. Sen, R., A. Farris, and P. Guerra. 2013. Benchmarking apache accumulo bigdata distributed table store using its continuous test suite. In 2013 IEEE International Congress on Big Data, 334–341.

    Google Scholar 

  14. Sivaraman, S., and M.M. Trivedi. 2013. Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems 14 (4): 1773–1795.

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute for Applied Computing (IAC-CNR), Via dei Taurini 19, Rome, Italy

    Massimo Bernaschi, Alessandro Celestini, Stefano Guarino, Flavio Lombardi & Enrico Mastrostefano

Authors
  1. Massimo Bernaschi
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  2. Alessandro Celestini
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  3. Stefano Guarino
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  4. Flavio Lombardi
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  5. Enrico Mastrostefano
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Corresponding author

Correspondence to Flavio Lombardi .

Editor information

Editors and Affiliations

  1. Italian National Police, Rome, Italy

    Fabio Leuzzi

  2. University of Bari, Bari, Italy

    Stefano Ferilli

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Bernaschi, M., Celestini, A., Guarino, S., Lombardi, F., Mastrostefano, E. (2018). Traffic Data: Exploratory Data Analysis with Apache Accumulo. In: Leuzzi, F., Ferilli, S. (eds) Traffic Mining Applied to Police Activities. TRAP 2017. Advances in Intelligent Systems and Computing, vol 728. Springer, Cham. https://doi.org/10.1007/978-3-319-75608-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-75608-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75607-3

  • Online ISBN: 978-3-319-75608-0

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