Method of monitoring the condition of road surface based on accelerometer signals and fuzzy logic apparatus

Purpose of research. The purpose of this research is to improve the accuracy of monitoring the condition of the road surface of an urban agglomeration by analyzing accelerometer signals in real time based on the developed integrated method for assessing evenness indicators.

Methods. The following methods were used in this research: analysis of existing methods for monitoring road surface conditions; methods and algorithms for filtering accelerometer signal noise (a model for preprocessing accelerometer signals was developed and described, including a Butterworth low-pass filter, a median filter, an exponential smoothing method, and calculation of threshold values); fuzzy logic algorithms (a model for classifying road surface conditions into 5 categories was developed); simulation modeling (test runs were conducted using the author's simulation model developed in the Unity environment).

Results. The presented method provides automated monitoring of the road surface condition with an accuracy of at least 93%, and the possibility of integration into the smart city system. The method allows monitoring the road surface condition in real time, and the classification of the road surface condition is of a recommendatory nature for road repairs. The prospects of the research include conducting a full-scale experiment, visualizing data with reference to a city map, and using trajectory clustering algorithms to determine the general trajectories of a vehicle when going around uneven surfaces.

Conclusion. Based on the developed method of monitoring the condition of the road surface, an integrated assessment of the compliance of the accuracy of monitoring the condition of the road surface of at least 93% was obtained.

1. Majidifard H., Jin P., Adu-Gyamfi Y., Buttlar W.G. Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses. Transp. Res. Rec. 2020; (2674): 328–339.

2. Sayers M. W. On the calculation of international roughness index from longitudinal road profile. Transportation Research Record. 1995; (1501).

3. Badanis K. Ye. Big Data Technology and Artificial Intelligence in Automobile Traffic Management. In: XXXVII Mezhdunarodnaya nauchno-prakticheskaya konferentsiya «Sovremennaya nauka: aktual'nye voprosy, dostizheniya i innovatsii» = XXXVII International Scientific and Practical Conference "Modern Science: Current Issues, Achievements and Innovations". Penza; 2024. P. 73-77. (In Russ.).

4. Ndoye M., Vanjari S., Huh H., Krogmeier J., Bullock D., Hedges C., Adewunmi A. Sensing and Signal Processing for a Distributed Pavement Monitoring System. 2006 IEEE 12th Digital Signal Processing Workshop & 4th IEEE Signal Processing Education Workshop, 2006. P. 162–167.

5. Silva G. D. De, Thilakarathna K. M., Perera R. S., Keppitiyagama C. I., Laxman N. M., Avenue R., Lanka S. Automated Pothole Detection System. In 9th International Information Technology Conference, 2008.

6. Alessandroni G., L. Klopfenstein C., Delpriori S., Dromedari M., Luchetti G., Paoli- ni B. D., Seraghiti A., Lattanzi E., Freschi V., Carini A., Bogliolo A. SmartRoadSense : Collaborative Road Surface Condition Monitoring, Ubicomm, 2014. P. 210–215.

7. Astarita V., Caruso M. V., Danieli G., Festa D. C., Giofrè V. P., Iuele T., Vaiana R. A Mobile Application for Road Surface Quality Control: UNIquALroad. In: Procedia - Social and Behavioral Sciences, Proceedings of EWGT2012 - 15th Meeting of the EURO Working Group on Transportation. 2012; 54: 1135–1144.

8. Vasiliev M. D., Degtyarev A. V., Chekurov A. Yu. Application of built-in smartphone sensors for detection and analysis of road unevenness violations. Sovremennye naukoemkie tekhnologii = Modern high technologies. 2022; (12): 20-25. (In Russ.).

9. Radopoulou S.-C., Brilakis I. Improving Road Asset Condition Monitoring. Transp. Res. Procedia. 2016; 14: 3004–3012.

10. Nakanishi Y., Kaneta T., Nishino S. A Review of Monitoring Construction Equipment in Support of Construction Project Management. Front. Built Environ. 2022; 7: 632593.

11. Khahro S.H.; Javed Y.; Memon Z.A. Low-Cost Road Health Monitoring System: A Case of Flexible Pavements. Sustainability. 2021; (13): 10272.

12. Surin V. I., Volkova Z. S. Algorithm for approximating experimental data based on a low-pass filter with a finite impulse response. Informatsionnye tekhnologii v proektirovanii i proizvodstve = Information technologies in design and production. 2020; (4): 25-29 (In Russ.).

13. Gelper S., Fried R., Croux C. Robust forecasting with exponential and Holt–Winters smoothing. Journal of forecasting. 2010; 29(3): 285-300.

14. Kolbas Yu. Yu., Kurdybanskaya A. I. Application of digital filters to reduce random error in readings of laser gyroscopes and pendulum accelerometers. Vestnik MGTU im. N.E. Baumana. Seriya «Priborostroenie» = Bulletin of Bauman Moscow State Technical University. Series Instrument Engineering. 2018; (2). (In Russ.).

15. Johnson D., Johnson J., Moore G. Handbook of active filters. Moscow: Energoatomizdat; 1983. 128 p. (In Russ.).

16. Smith S. W. The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Pub., 2002.

17. Cook J. R., Gourley C. S. A framework for the appropriate use of marginal materials World road association (PIARC)-technical committee c12 seminar in Mongolia, 2002.

18. Bovik A. C. Handbook of image and video processing. Academic press, 2010.

19. Gonzalez R. C. Digital Image Processing. Pearson Education India, 2009.

20. Bhoraskar R., et al. Wolverine: Traffic and road condition estimation using smartphone sensors. 2012 fourth international conference on communication systems and networks (COMSNETS 2012). IEEE, 2012. P. 1-6.

21. Melnik V. G. Estimation of the exponential smoothing coefficient of navigation data based on measurement results. Ekspluatatsiya morskogo transporta = Operation of sea transport. 2014; (1): 29-32. (In Russ.).

22. Brown R. G. Smoothing, forecasting and prediction of discrete time series. Courier Corporation, 2004.

23. Winters P. R. Forecasting sales by exponentially weighted moving averages. Management science. 1960; 6 (3): 324-342.

24. Gelper S., Fried R., Croux C. Robust forecasting with exponential and Holt–Winters smoothing. Journal of forecasting. 2010; 29(3): 285-300.

25. Mitsuishi T. Definition of centroid method as defuzzification. Formalized Mathematics. 2022; 30(2): 125-134.

26. Chakraverty S., et al. Defuzzification. Concepts of soft computing: Fuzzy and ANN with programming, 2019. P. 117-127.

27. Runkler T. A., Glesner M. DECADE–Fast centroid approximation defuzzification for real time fuzzy control applications. Proceedings of the 1994 ACM symposium on Applied computing, 1994. P. 161-165.

28. Wang Y. M. Centroid defuzzification and the maximizing set and minimizing set ranking based on alpha level sets. Computers & Industrial Engineering. 2009; 57(1): 228-236.

29. Vlachos M., Kollios G., Gunopulos D. Discovering similar multidimensional trajectories. In: Proceedings 18th international conference on data engineering. IEEE, 2002. P. 673-684.

30. Mukhametzyanov I. Z. Fuzzy logical inference and fuzzy hierarchy process analysis in decision support systems: application to reliability assessment of technical systems. Kibernetika i programmirovanie = Cybernetics and programming. 2017; (2): 59-77. (In Russ.).

31. Kofman A. Introduction to the Theory of Fuzzy Sets. Moscow: Radio i svyaz'; 1982. 432 p. (In Russ.).

32. Kaufman А., Gupta M. M. Fuzzy Mathematical Models in Engineering and Management Science, North-Holland, Amsterdam; 1988. 338 p.

33. Gourley C. S. World Road Association (PIARC) - Technical Committee C12 Seminar in Mongolia, June 2002 A Framework for the Appropriate Use of Marginal Materials JR Cook.

34. PIARC – World Road Association. Available at: https://www.piarc.org/en/ (accessed 15.04.2025)

35. Pudova N.V., Nikitin V.V. Analysis of the values of the Spearman rank correlation coefficient. Ekonomicheskii analiz: teoriya i praktika = Economic Analysis: Theory and Practice. 2004; (3): 52-56. (In Russ.).

36. Kosheleva N. N. Correlation analysis and its application for calculating Spearman's rank correlation. Aktual'nye problemy gumanitarnykh i estestvennykh nauk = Actual problems of humanitarian and natural sciences. 2012; (5): 23-26. (In Russ.).