Road Anomaly Detection Using Smartphone: A Brief Analysis

  • Van Khang NguyenEmail author
  • Éric Renault
  • Viet Hai Ha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11005)


Identification of road anomaly not only helps drivers to reduce the risk, but also support for road maintenance. Arguably, with the popularity of smartphones including multiple sensors, many road anomaly detection systems using mobile phones have been proposed. This paper aims at analyzing a number of typical road anomaly detection methods in terms of resource requirements, energy consumption, fitness conditions. From these measurements, we suggest some improvement directions to build road anomaly detection algorithms appropriate for smartphones.


Road anomaly Pothole Road condition Sensors network 


  1. 1.
  2. 2.
    Alpaydin, E.: Introduction to Machine Learning. Adaptive Computation and Machine Learning, 2nd edn. MIT Press, New York (2010)zbMATHGoogle Scholar
  3. 3.
    Bhoraskar, R., Vankadhara, N., Raman, B., Kulkarni, P.: Wolverine: traffic and road condition estimation using smartphone sensors. In: 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS), pp. 1–6. IEEE (2012)Google Scholar
  4. 4.
    Chugh, G., Bansal, D., Sofat, S.: Road condition detection using smartphone sensors: a survey. Int. J. Electron. Electr. Eng. 7(6), 595–602 (2014)Google Scholar
  5. 5.
    Cong, F., et al.: Applying wavelet packet decomposition and one-class support vector machine on vehicle acceleration traces for road anomaly detection. In: Guo, C., Hou, Z.-G., Zeng, Z. (eds.) ISNN 2013. LNCS, vol. 7951, pp. 291–299. Springer, Heidelberg (2013). Scholar
  6. 6.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  7. 7.
    Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia (1992)CrossRefGoogle Scholar
  8. 8.
    Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., Balakrishnan, H.: 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, pp. 29–39. ACM (2008)Google Scholar
  9. 9.
    Feldman, M.: Signal Demodulation. Wiley, New York (2011)CrossRefGoogle Scholar
  10. 10.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, pp. 439–444. University of Michigan Press, Ann Arbor (1975)Google Scholar
  11. 11.
    Kim, T., Ryu, S.K.: Review and analysis of pothole detection methods. J. Emerg. Trends Comput. Inf. Sci. 5(8), 603–608 (2014)Google Scholar
  12. 12.
    Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., Selavo, L.: Real time pothole detection using android smartphones with accelerometers. In: 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), pp. 1–6. IEEE (2011)Google Scholar
  13. 13.
    Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 323–336. ACM (2008)Google Scholar
  14. 14.
    Nason, G.P., Silverman, B.W.: The stationary wavelet transform and some statistical applications. In: Antoniadis, A., Oppenheim, G. (eds.) Wavelets and Statistics. LNS, vol. 103, pp. 281–299. Springer, New York (1995). Scholar
  15. 15.
    Perttunen, M., et al.: Distributed road surface condition monitoring using mobile phones. In: Hsu, C.-H., Yang, L.T., Ma, J., Zhu, C. (eds.) UIC 2011. LNCS, vol. 6905, pp. 64–78. Springer, Heidelberg (2011). Scholar
  16. 16.
    Seraj, F., van der Zwaag, B.J., Dilo, A., Luarasi, T., Havinga, P.: RoADS: a road pavement monitoring system for anomaly detection using smart phones. In: Atzmueller, M., Chin, A., Janssen, F., Schweizer, I., Trattner, C. (eds.) Big Data Analytics in the Social and Ubiquitous Context. LNCS (LNAI), vol. 9546, pp. 128–146. Springer, Cham (2016). Scholar
  17. 17.
    Tanaka, N., Okamoto, H., Naito, M.: Detecting and evaluating intrinsic nonlinearity present in the mutual dependence between two variables. Phys. D: Nonlinear Phenom. 147(1–2), 1–11 (2000)CrossRefGoogle Scholar
  18. 18.
    Vittorio, A., Rosolino, V., Teresa, I., Vittoria, C.M., Vincenzo, P.G., et al.: Automated sensing system for monitoring of road surface quality by mobile devices. Procedia - Soc. Behav. Sci. 111, 242–251 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Van Khang Nguyen
    • 1
    • 2
    Email author
  • Éric Renault
    • 1
  • Viet Hai Ha
    • 2
  1. 1.Institut Mines-Télécom/Télécom SudParis, CNRS UMR 5157 SAMOVAREvry CedexFrance
  2. 2.College of EducationHue UniversityHueVietnam

Personalised recommendations