Detection of road pavement quality using statistical clustering methods

  • Joachim DavidEmail author
  • Toon De Pessemier
  • Luc Dekoninck
  • Bert De Coensel
  • Wout Joseph
  • Dick Botteldooren
  • Luc Martens


Road owners are concerned with the state of the road surface and they try to reduce noise coming from the road as much as possible. Using sound level measuring equipment installed inside a car, we can indirectly measure the road pavement state. Noise inside a car is made up of rolling noise, engine noise and other confounding factors. Rolling noise is influenced by noise modifiers such as car speed, acceleration, temperature and road humidity. Engine noise is influenced by car speed, acceleration, and gear shifts. Techniques need to be developed which compensate for these modifying factors and filter out the confounding noise. This paper presents a hierarchical clustering method resulting in a mapping of the road pavement quality. We present the method using a dataset recorded in multiple cars under different circumstances. The data has been retrieved by placing a Raspberry Pi device within these cars and recording the sound and location during various trips at different times. The sound data of our dataset was then corrected for correlation with speed and acceleration. Furthermore, clustering techniques were used in order to estimate the type and condition of the pavement using this set of noise measurements. The algorithms were run on a small dataset and compared to a ground truth which was derived from visual observations. The results were best for a combination of Generalised Additive Model (GAM) correction on the data combined with hierarchical clustering. A connectivity matrix merging points close to each other further enhances the results for road pavement quality detection, and results in a road type detection rate around 90%.


Clustering methods Road noise Pavement quality K-means Ward’s method Road detection 



This work was executed within the MobiSense research project. MobiSense is co-financed by IMEC and received support from Flanders Innovation & Entrepreneurship. Special thanks are given to the company ASAsense for providing measured road noise data.


  1. Bishop, C.M. (2006). Pattern recognition and machine learning (information science and statistics). Berlin: Springer. ISBN 0387310738.zbMATHGoogle Scholar
  2. Defrance, J., Salomons, E., Noordhoek, I., Heimann, D., Plovsing, B., Watts, G., Jonasson, H., Zhang, X., Premat, E., Schmich, I., Aballea, F., Baulac, M., de Roo, F. (2007). Outdoor sound propagation reference model developed in the European Harmonoise project. Acta Acustica United with Acustica, 93 (2), 213–227. ISSN 1610-1928.Google Scholar
  3. Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38. ISSN 00359246. Scholar
  4. Ejsmont, J., & Sandberg, U. (2002). Tyre/road noise. Reference book. Hisa: INFORMEX Ejsmont & Sandberg.Google Scholar
  5. Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., Balakrishnan, H. (2008). The pothole patrol: Using a mobile sensor network for road surface monitoring. In Proceedings of the 6th international conference on mobile systems, applications, and services, MobiSys ’08. ISBN 978-1-60558-139-2, (pp. 29–39). New York: ACM.
  6. Hastie, T., Tibshirani, R., Friedman, J. (2001). The elements of statistical learning. Springer series in statistics. New York: Springer.zbMATHGoogle Scholar
  7. Kongrattanaprasert, W., Nomura, H., Kamakura, T., Ueda, K. (2009). Detection of road surface conditions using tire noise from vehicles. IEEJ Transactions on Industry Applications, 129(7), 761–767. Scholar
  8. Lex Brown, A. (2015). Effects of road traffic noise on health: From burden of disease to effectiveness of interventions. Procedia Environmental Sciences, 30, 3–9. Scholar
  9. Masino, J., Foitzik, M.-J., Frey, M., Gauterin, F. (2017). Pavement type and wear condition classification from tire cavity acoustic measurements with artificial neural networks. The Journal of the Acoustical Society of America, 141(6), 4220–4229. Scholar
  10. Newson, P., Krumm, J., Microsoft Research. (2009). Hidden markov map matching through noise and sparseness. In 17th ACM SIGSPATIAL international conference on advances in geographic information systems. (pp. 336–343).
  11. None. (1997). Acoustics ? measurement of the influence of road surfaces on traffic noise ? Part 1: statistical pass-by method. Standard, international organization for standardization.Google Scholar
  12. None. (2017). Acoustics ? measurement of the influence of road surfaces on traffic noise ? Part 2: the close-proximity method. Standard, international organization for standardization.Google Scholar
  13. Ouis, D. (2001). Annoyance from road traffic noise: a review. Journal of Environmental Psychology, 21(1), 101–120. ISSN 02724944.CrossRefGoogle Scholar
  14. Paje, S.E., Bueno, M., Terán, F., Viñuela, U. (2007). Monitoring road surfaces by close proximity noise of the tire/road interaction. The Journal of the Acoustical Society of America, 122(5), 2636. Scholar
  15. Paje, S.E., Bueno, M., Viñuela, U., Terán, F. (2009). Toward the acoustical characterization of asphalt pavements: Analysis of the tire/road sound from a porous surface. The Journal of the Acoustical Society of America, 125(1), 5–7. Scholar
  16. Pallas, M.-A., Bérengier, M., Chatagnon, R., Czuka, M., Conter, M., Muirhead, M. (2016). Towards a model for electric vehicle noise emission in the european prediction method CNOSSOS-EU. Applied Acoustics, 113, 89–101. Scholar
  17. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12 (Oct), 2825–2830.MathSciNetzbMATHGoogle Scholar
  18. Peeters, B., & van Blokland, G.J. (2007). The noise emission model for European road traffic. IMAGINE deliverable, 11(11).Google Scholar
  19. Salomons, E., van Maercke, D., Defrance, J., de Roo, F. (2011). The harmonoise sound propagation model. Acta Acustica united with Acustica, 97(1), 62–74. Scholar
  20. Singh, G., Bansal, D., Sofat, S., Aggarwal, N. (2017). Smart patrolling: an efficient road surface monitoring using smartphone sensors and crowdsourcing. Pervasive and Mobile Computing, 40, 71–88. Scholar
  21. Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. ISSN 01621459. Scholar
  22. Zhang, Y., Mcdaniel, J., Wang, M.L. (2014). Pavement macrotexture estimation using principal component analysis of tire/road noise. Proceedings of SPIE - The International Society for Optical Engineering, 9063.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.INTEC-WAVES/IMECGhent UniversityGentBelgium

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