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Machine Learning for Pavement Friction Prediction Using Scikit-Learn

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Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

During the last decades, the advent of Artificial Intelligence (AI) has been taking place in several technical and scientific areas. Despite its success, AI applications to solve real-life problems in pavement engineering are far from reaching its potential. In this paper, a Python machine learning library, scikit-learn, is used to predict asphalt pavement friction. Using data from the Long-Term Pavement Performance (LTPP) database, 113 different sections of asphalt concrete pavement, spread all over the United States, were selected. Two machine learning models were built from these data to predict friction, one based on linear regression and the other on regularized regression with lasso. Both models showed to be feasible and perform similarly. According to the results, initial friction plays an essential role in the way friction evolves over time. The results of this study also showed that scikit-learn can be a versatile tool to solve pavement engineering problems. By applying machine learning methods to predict asphalt pavements friction, this paper emphasizes how theory and practice can be effectively coupled to solve real-life problems in contemporary transportation.

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Author information

Authors and Affiliations

  1. LNEC, Lisbon, Portugal

    Pedro Marcelino, Maria de Lurdes Antunes & Eduardo Fortunato

  2. IST, Lisbon, Portugal

    Marta Castilho Gomes

Authors
  1. Pedro Marcelino
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  2. Maria de Lurdes Antunes
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  3. Eduardo Fortunato
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  4. Marta Castilho Gomes
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Corresponding author

Correspondence to Pedro Marcelino .

Editor information

Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Marcelino, P., de Lurdes Antunes, M., Fortunato, E., Gomes, M.C. (2017). Machine Learning for Pavement Friction Prediction Using Scikit-Learn. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_28

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65339-6

  • Online ISBN: 978-3-319-65340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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