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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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
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