Vehicle’s Weight Estimation Using Smartphone’s Acceleration Data to Control Overloading

  • Phong X. NguyenEmail author
  • Takayuki Akiyama
  • Hiroki Ohashi
  • Masaaki Yamamoto
  • Akiko Sato


We propose an overloading control system with a novel method to estimate vehicle weight using the sensor data from a smartphone mounted on the vehicle. The conventional method based on fixed weigh stations has limited coverage, and is expensive to install and maintain. Our proposed system overcomes these limitations by using smartphones, which are portable and cheaper. A multiple linear regression model is created using vertical acceleration statistical features and loading status classification as explanatory variables to estimate the vehicle’s weight. A pilot experiment estimating a trolley’s weight was followed by an experiment estimating an actual vehicle’s weight to verify the feasibility of using our method. We achieved average error of 593 kg, which accounted for 5.89% of the true average vehicle’s weight.


Overloading control Smartphone Vehicle’s weight estimation Classification Multiple linear regression 


  1. 1.
    Larsen, O.I., Odeck, J., Kjerkerit, A.: The economic impact of enforcing axle load regulation – the case of Zambia. ISBN 978-82-7830-129-6, ISSN 0806-0789 (2008)Google Scholar
  2. 2.
    Department of Transport of Republic of South Africa: The damaging effects of overloaded heavy vehicles on roads. ISBN 1-86844-285-3 (1997)Google Scholar
  3. 3.
    Moran, G., Erbs, M., Tester, B.: The effects of overloading on road assets, (online), available from <>.
  4. 4.
    Sun, C., Ritchie, S.G., Tsai, K.: Algorithm development for derivation of section-related measures of traffic system performance using inductive loop detectors. 77th Annual Meeting of the Transportation Research Board, Washington, D.C (1998)Google Scholar
  5. 5.
    Kim, S.W., et al.: A new method for accurately estimating the weight of moving vehicles using piezoelectric sensors and adaptive-footprint tire model. Veh. Syst. Dyn. 39(2), (2003)Google Scholar
  6. 6.
    Deesomsuk, T., Pinkaew, T.: “Effectiveness of vehicle weight estimation from bridge weigh-in-motion”, Hindawi publishing corporation. Advances in Civil Engineering. 2009, 312034 (2009)CrossRefGoogle Scholar
  7. 7.
    Bushman, R., Pratt, A.J.: Weigh in motion technology - economics and performance, (online), available from <> (accessed in Mar 2014).
  8. 8.
    Karim, M.R., Ibrahim, N.I., Saifizul, A.A., Yamanakam, H.: Effectiveness of vehicle weight enforcement in a developing country using weigh-in-motion sorting system considering vehicle by-pass and enforcement capability. IATSS Research. (2013). doi: 10.1016/j.iatssr.2013.06.004, (accessed in Mar 2014)
  9. 9.
    Iowa State University, Center of Transportation Research and Education: “WIM Accuracy and Quality Assurance Discussion”, (online), available from Accessed in March 2014
  10. 10.
    Kadlecek, B., Pejša,L., Svítek, M.: Experimental verification of the method used in gauging weight of moving vehicles. Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems (2005)Google Scholar
  11. 11.
    Nguyen, P. et al.: Vehicle weight estimation using smartphone acceleration data to control overloading. Proceedings of 22nd Intelligent Transportation System World Congress (2015)Google Scholar
  12. 12.
    Ohashi, H., Akiyama, T., Yamamoto, M., Sato, A.: Modality classification method based on the model of vibration generation while vehicles are running. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science (2013)Google Scholar
  13. 13.
    Asakawa, H., Nagayama, T., Fujino, Y., Nishikawa, T., Akimoto, T., Izumi, K.: Development of a simple pavement diagnostic system using dynamic responses of an ordinary vehicle. Journal of Japan Society of Civil Engineers, Ser. E1(Pavement Engineering) 01/2012. 68(1), 20–31 (2012). doi: 10.2208/jscejpe.68.20 Google Scholar
  14. 14.
    Whittaker, E.T., Robinson, G.: The method of least squares. Ch. 9 in the calculus of observations: a treatise on numerical mathematics, 4th edn. Dover, New York (1967)Google Scholar
  15. 15.
    Hastie, T.J., Pregibon, D.: Generalized linear models. In: Chambers, J.M., Hastie, T.J. (eds.) Statistical Models in S. Wadsworth & Brooks/Cole, Pacific Grove (1992)Google Scholar
  16. 16.
    Venables, W.N., Ripley, B.D.: Modern applied statistics with S, 4th edn. Springer, New York (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Stone, M.: Cross-validatory choice and assessment of statistical predictions. Journal Royal Statistic Soceity. 36(2), 111–147 (1974)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Ban, X.: Vehicle classification using mobile sensors. Research and Innovative Technology Administration / USDOT (2013)Google Scholar
  19. 19.
    Couchman, D.: What is the difference between sprung weight and unsprung weight?, (online), available from <>
  20. 20.
    Lachenbruch, P.A., Mickey, M.R.: Estimation of error rates in discriminant analysis. Technometrics. 10(1), (1968)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Phong X. Nguyen
    • 1
    Email author
  • Takayuki Akiyama
    • 1
  • Hiroki Ohashi
    • 2
  • Masaaki Yamamoto
    • 1
  • Akiko Sato
    • 3
    • 4
  1. 1.Hitachi Ltd., Center for Technology Innovation – Systems EngineeringTokyoJapan
  2. 2.European R&D group, Hitachi Europe GmbHDüsseldorfGermany
  3. 3.RWTH Aachen UniversityAachenGermany
  4. 4.Hitachi Asia Ltd.SingaporeSingapore

Personalised recommendations