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Insights for Urban Road Safety: A New Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data

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Periodic Pattern Mining

Abstract

Traffic congestion is a significant challenge that cities worldwide have to tackle as it poses many potential risks. Building a predictive system to anticipate future congestion would alleviate them. Furthermore, if the system can discover future frequent traffic congestion patterns, authorities can build reaction plans to deal with congestion more effectively. Unfortunately, other works have failed to achieve it. This study proposes a novel dynamic system to address the mentioned problem. It integrates a traffic congestion prediction model and a periodic-frequent pattern discovery algorithm. In particular, we utilize our novel Fusion-3DCNN deep learning model and a periodic-frequent pattern discovery algorithm in the system. The former predicts long-term traffic congestion on citywide mesh codes using multi-modal urban sensing data, while the latter identifies sets of mesh codes that are regularly predicted to have heavy traffic congestion. Experimental results on a real-world dataset collected in Kobe City, Japan, from 2014 to 2015 show that our framework is efficient in terms of accuracy and time.

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Notes

  1. 1.

    http://www.stat.go.jp/english/data/mesh/02.html.

  2. 2.

    https://keras.io/.

  3. 3.

    https://www.tensorflow.org/.

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Acknowledgements

We appreciate the contribution of Ngoc-Thanh Nguyen, currently Ph.D. students of Western Norway University of Applied Sciences, to this chapter.

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Authors and Affiliations

  1. National Institute of Information and Communications Technology, Tokyo, Japan

    Minh-Son Dao, R. Uday Kiran & Koji Zettsu

  2. University of Aizu, Fukushima, Japan

    R. Uday Kiran

Authors
  1. Minh-Son Dao
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  2. R. Uday Kiran
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  3. Koji Zettsu
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Corresponding author

Correspondence to Minh-Son Dao .

Editor information

Editors and Affiliations

  1. Division of Information Systems, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    R. Uday Kiran

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    Jose M. Luna

  4. Department of Computer Science, Electrical Engineering, and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  5. Department of Computer Science, Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

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Dao, MS., Uday Kiran, R., Zettsu, K. (2021). Insights for Urban Road Safety: A New Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data. In: Kiran, R.U., Fournier-Viger, P., Luna, J.M., Lin, J.CW., Mondal, A. (eds) Periodic Pattern Mining . Springer, Singapore. https://doi.org/10.1007/978-981-16-3964-7_14

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  • DOI: https://doi.org/10.1007/978-981-16-3964-7_14

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  • Online ISBN: 978-981-16-3964-7

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