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Estimating Road Surface Condition Using Crowdsourcing

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Information Search, Integration, and Personlization (ISIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 760))

Abstract

Road surface conditions have a significant impact on transport safety and driving comfort, particularly in snowy areas. This paper proposes a new method for estimating road surface conditions by using a motion sensor embedded in a smartphone. The method is based on a mobile sensing framework that can collect sensor data using crowdsourcing. In this study, we have defined new road surface conditions as the estimation target, which takes into account both the substance that covers the road surface, and the shape of the road surface itself. The paper also describes a method of feature selection, comprising two steps: First, an initial feature set is directly calculated using various features published in previous studies, using raw sensor data. Second, three feature selection algorithms are compared: Principal Component Analysis (PCA), Relief-F, and Sequential Forward Floating Search (SFFS), and the most effective of the three chosen. In this study, the SFFS algorithm showed higher accuracy than the others. The road surface condition classification was performed across different speed ranges using the Random Forest Classifier, and results show that the best accuracy, of about 91%, was obtained in the 50 km/h–80 km/h range.

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Notes

  1. 1.

    https://itunes.apple.com/app/drive-around-the-corner./id1053216595.

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Acknowledgments

This research was supported by “Research and Development on Fundamental and Utilization Technologies for Social Big Data” of the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan. And also it was partly supported by the CPS-IIP Project in the research promotion program “Research and Development for the Realization of Next-Generation IT Platforms” of the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT).

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Authors and Affiliations

  1. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan

    Bin Piao, Kenro Aihara, Atsuhiro Takasu & Jun Adachi

  2. The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan

    Akira Kinoshita

Authors
  1. Bin Piao
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  2. Kenro Aihara
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  3. Akira Kinoshita
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  4. Atsuhiro Takasu
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  5. Jun Adachi
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Corresponding author

Correspondence to Bin Piao .

Editor information

Editors and Affiliations

  1. Saint Lab ETIS, Université de Cergy-Pontoise, Pontoise, France

    Dimitris Kotzinos

  2. Laboratoire ETIS, Université de Cergy Pontoise, Cergy Pontoise, France

    Dominique Laurent

  3. Dept Informatique INSA Lyon, Université de Lyon, Villeurbanne Cedex, France

    Jean-Marc Petit

  4. LRI, University of Paris, Orsay, France

    Nicolas Spyratos

  5. Graduate School of Information Science Knowledge Media Laboratory, Hokkaido University, Sapporo, Japan

    Yuzuru Tanaka

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Piao, B., Aihara, K., Kinoshita, A., Takasu, A., Adachi, J. (2017). Estimating Road Surface Condition Using Crowdsourcing. In: Kotzinos, D., Laurent, D., Petit, JM., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personlization. ISIP 2016. Communications in Computer and Information Science, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-68282-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-68282-2_5

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