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Spatio-temporal Road Condition Forecasting with Markov Chains and Artificial Neural Networks

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Hybrid Artificial Intelligence Systems (HAIS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5271))

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Abstract

Preservation of the road assets value in an efficient manner is an important aim for developed road administrations. The task requires accurate road maintenance that is planned in advance. Forecasting road condition in the future is a prerequisite for optimisation of maintenance treatments. In this study two hybrid methods are introduced for forecasting road roughness and rutting. Markovian models outperform artificial neural network models and roughness can be forecast more accurately than rutting.

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References

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Author information

Authors and Affiliations

  1. Sirway Ltd., Finland

    Konsta Sirvio

  2. Dept. of Information and Computer Science, Helsinki University of Technology, Finland, P.O. Box 5400, FI-02015 TKK, Finland

    Jaakko Hollmén

Authors
  1. Konsta Sirvio
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  2. Jaakko Hollmén
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, 7491, Trondheim, Norway

    Ajith Abraham

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton,, T6G 2V4, Alberta, Canada

    Witold Pedrycz

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© 2008 Springer-Verlag Berlin Heidelberg

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Sirvio, K., Hollmén, J. (2008). Spatio-temporal Road Condition Forecasting with Markov Chains and Artificial Neural Networks. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_26

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  • DOI: https://doi.org/10.1007/978-3-540-87656-4_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87655-7

  • Online ISBN: 978-3-540-87656-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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