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Features of Using the Method of Normalised Range and Fractal Analysis in Studying the Car Traffic Flow Intensity

https://doi.org/10.30932/1992-3252-2024-22-3-2

Abstract

The relevance of studying the methods of studying traffic flows is due to the need to analyse their features, determine the allowable areas of their application in solving practical problems of the transport sector.

The objective of the work is to identify the results of application of modern methods of time series analysis that use the values of intensities of car traffic flows on the urban street-and-road network. The subject of the study is associated with the calculated Hurst and fractal dimension indices (fractal analysis), as well as with checking the validity of the quantitative relationship of these indicators, specified by B.Mandelbrot and used in applied research, on real data on intensity of car traffic flows. Digital data for the study were obtained using «Azimuth» stationary measuring software and hardware photo and video recording complexes, located on the street-and-road network of the city.

The study has found that anomalous values of key indicators are encountered when using the normalised range method and fractal analysis: the Hurst exponent takes values outside the usually defined range, the relationship between the fractal dimension and the Hurst exponent does not fully correspond to the known B. Mandelbrot set. It seems necessary to conduct a deep and thorough study of the results obtained when using the above and, possibly, other methods for studying the intensity of traffic flows on street-and-road networks.

About the Author

M. G. Boyarshinov
Perm National Research Polytechnic University
Russian Federation

Boyarshinov, Mikhail G., D.Sc. (Eng), Professor at the Department of Automobiles and Technological Machines 

Perm

Web of Science Researcher ID: ACE-0166-2022; Scopus Author ID: 6506008407; Russian Science Citation Index Author ID: 79853; ColabID: R-38610-17352-TA83O, Google Scholar: Y4AT3SUAAAAJ



References

1. Mandelbrot, B. The Fractal Geometry of Nature [Edition in Russian]. Moscow, Institute of Computer Research, 2002, 656 p. ISBN 5-93972-108-7.

2. Feder, J. Fractals [Edition in Russian]. Moscow, Mir publ., 1991, 254 p. ISBN 5-03-001712-7.

3. Crownover, R. M. Introduction to fractals and chaos [Edition in Russian]. Moscow, Postmarket publ., 2000, 354 p. ISBN 5-901095-03-0.

4. Grahovac, D., Leonenko, N. N., Taqqu, M. S. Scaling Properties of the Empirical Structure Function of Linear Fractional Stable Motion and Estimation of Its Parameters. Journal of Statistical Physics, 2015, Vol. 158 (1), pp. 105– 119. DOI: 10.1007/s10955-014-1126-4.

5. Lyubushin, A. A. Great Japan Earthquake Forecast [Prognoz Velikogo Yaponskogo zemletryaseniya]. Priroda, 2012, Iss. 8 (1164), pp. 23–33. [Electronic resource]: https://elibrary.ru/download/elibrary_18042955_47303750.pdf. Last accessed 16.01.2024. EDN: PEVRQB.

6. Hasanov, A. B., Abbasova, G. G.Analysis of the fractal structure and stochastic distribution of pores in oil and gas reservoirs. Perm Journal of Petroleum and Mining Engineering, 2019, Vol. 19, Iss. 3, pp. 228–239. DOI: 10.15593/2224-9923/2019.3.3.

7. Smirnov, V. V., Spiridonov, F. F. Fractal models of stochastic processes [Fraktalnie modeli stokhasticheskikh protsessov]. South Siberian Scientific Bulletin, 2013, Iss. 1 (3), pp. 99–102. [Electronic resource]: https://elibrary.ru/download/elibrary_19107961_73506142.pdf. Last accessed 16.01.2024. EDN: QCDQDT.

8. Pashchenko, F. F., Amosov, O. S., Muller, N. V. Structural-parametric identification of time series using fractal and wavelet analysis [Strukturno-parametricheskaya identifikatsiya vremennogo ryada s primeneniem fraktalnogo i veivlet-analiza]. Information Science and Control Systems, 2015, Iss. 2 (44), pp. 80–88. [Electronic resource]: https://www.elibrary.ru/item.asp?edn=twnbmr&ysclid=m3ok83dhjp774512399. Last accessed 17.01.2024. EDN: TWNBMR.

9. Klikushin, Yu. N.Method of fractal classification of complex signals [Metod fraktalnoi klassifikatsii slozhnykh signalov]. Zhurnal Radioelektroniki, 2000, Iss. 4, 6 p. [Electronic resource]: https://www.elibrary.ru/item.asp?id=15111685. Last accessed 16.01.2024. EDN: MSRXQN.

10. Neganov, V. A., Antipov, O. I., Neganova, E.V.Fractal analysis of time series describing qualitative transformation of systems, including disasters [Fraktalniy analiz vremennykh ryadov, opisyvayushchikh kachestvennie preobrazovaniya system, vklyuchaya katastrofy]. Fizika volnovykh protsessov i radiotekhnicheskie sistemy, 2011, Vol. 14, Iss. 1, pp. 105– 110. [Electronic resource]: https://elibrary.ru/download/elibrary_16337899_76189553.pdf. Last accessed 16.01.2024. EDN: NTNOXH.

11. Krivonosova, E. K., Pervadchuk, V. P., Krivonosova, E. A. Comparison of fractal characteristics of time series of economic indicators [Sravnenie fraktalnykh kharakteristik vremennykh ryadov ekonomicheskikh pokazatelei]. Sovremennie problem nauki i obrazovaniya, 2014, Iss. 6, 113 p. [Electronic resource]: https://elibrary.ru/download/elibrary_22877127_95621621.pdf. Last accessed 16.01.2024. EDN: TGQDAZ.

12. Barabash, T. K., Maslovskaya, A.G.Computer modelling of fractal time series [Kompyuternoe modelirovanie fraktalnykh vremennykh ryadov]. Bulletin of Amur State University. Natural and economic sciences, 2010, Iss.49, pp https://elibrary.ru/contents.asp?id=33826785&selid=18986248.31–38. [Electronic resource]: https://vestnik.amursu.ru/wp-content/uploads/2017/12/N49_7.pdf. Last accessed 16.01.2024.

13. Garafutdinov, R. V., Akhunyanova, S. A.Adapted box-counting method for assessment of the fractal dimension of financial time series. Applied Mathematics and Control Issues, 2020, Iss. 3, pp. 185–218. DOI: 10.15593/2499-9873/2020.3.10.

14. Gerogiorgis, D. I. Fractal scaling in crude oil price evolution via Time Series Analysis (TSA) of historical data. Chemical Product and Process Modeling, 2009, Vol. 4, No. 5. DOI: https://doi.org/10.2202/1934-2659.1370.

15. Nekrasova, I. V.The Hurst index as a measure for the fractal structure and long-term memory of financial markets. International Research Journal, 2015, Iss. 7 (38), pp. 87–91. [Electronic resource]: https://research-journal.org/media/PDF/irj_issues/7–3–38.pdf#page=87. Last accessed 15.01.2024.

16. Simonov, P. M., Garrafutdinov, R. V. Modelling and forecasting of financial instruments dynamics using econometrics models and fractal analysis. Bulletin of Perm University. Economics, 2019, Vol. 14, Iss. 2, pp. 268–288. DOI: 10.17072/1994–9960–2019–2–268–288.

17. Generalova, A. A., Bychkov, D. S. Improving the aerodynamic properties of an intercity bus using fractal theory [Uluchshenie aerodunamicheskikh svoistv mezhdugorodnego avtobusa s primeneniem teorii fraktalov]. Models, systems, networks in economics, technology, nature and society, 2015, Iss. 2 (14), pp. 158–166. [Electronic resource]: https://elibrary.ru/download/elibrary_24107892_77708413.pdf. Last accessed 13.01.2024.

18. Karablin, O. V. On the fractal nature of city automobile traffic [O fraktalnom kharaktere avtomobilnogo trafika goroda]. Economy: yesterday, today, tomorrow, 2018, Vol.8, Iss. 9А, pp. 287–292. [Electronic resource]: http://www.publishing-vak.ru/file/archive-economy2018–9/32karablin.pdf. Last accessed 13.01.2024.

19. Pengjian Shang, Meng Wan, Santi Kama. Fractal nature of highway traffic data. Computers and Mathematics with Applications, 2007, Vol. 54, Iss. 1, pp. 107–116. DOI: doi.org/10.1016/j.camwa.2006.07.017.

20. Qiang Meng, Hooi Ling Khoo. Self-Similar Characteristics of Vehicle Arrival Pattern on Highways. Journal of Transportation Engineering, 2009, Vol. 135, Iss. 11, pp. 864–872. DOI: doi.org/10.1061/(ASCE)0733-947X(2009)135:11(864).

21. Xuewei Li, Pengjian Shang. Multifractal classification of road traffic flows. Chaos, Solitons and Fractals, 2007, Vol. 31, Iss. 5, pp. 1089–1094. DOI: doi.org/10.1016/j.chaos.2005.10.109.

22. David, S. A., Machado, J. A. T., Inacio, C. M. C., Valentim, C. A. A combined measure to differentiate EEG signals using fractal dimension and MFDFA-Hurst. Communications in Nonlinear Science Numerical Simulation, 2020, Vol. 84, 105170. DOI: https://doi.org/l0.l0l6/j.cnsns.2020.105170.

23. Erofeeva, E. S., Lyapunova, E. A., Oborin, V. A., Gileva, O. S., Naimark, O. B. Structural and functional analysis of dental hard tissues in assessing the quality of whitening technologies [Strukturno-funktsionalniy analiz tverdykh tkanei zubov v otsenke kachestva tekhnologii otbelivaniya]. Russian Journal of Biomechanics, 2010, Vol. 14, Iss. 2 (48), pp. 47–55. [Electronic resource]: https://www.elibrary.ru/download/elibrary_15105346_39403207.pdf. Last accessed 16.01.2024.

24. Boyarshinov, M. G.The method of normalised range for the analysis of traffic flow intensity [Metod normirovannogo razmakha dlya analiza intensivnosti transportnogo potoka]. Bulletin of the State Budgetary Institution «Scientific Center for Life Safety», 2020, Iss. 2 (46), pp. 35–46. [Electronic resource]: https://elibrary.ru/download/elibrary_44788054_61375863.pdf. Last accessed 15.01.2024.

25. Boyarshinov, M. G., Vavilin, A. S.The deterministic component of the traffic flow intensity. IOP Conference Series: Materials Science and Engineering, International Conference: Actual Issues of Mechanical Engineering (AIME 2020) 27th – 29th October 2020, Saint-Petersburg, Russian Federation, 2021, 1111 (1), 012013(10 p). DOI: 10.1088/1757-899X/1111/1/012013.

26. Boyarshinov, M. G., Vavilin, A. S., Shumkov, A. G. Using the complex of photo and video recording of traffic violations to identify deterministic and stochastic components of the traffic flow intensity. Intellekt. Innovacii. Investicii, 2021, Iss. 3, pp. 61–71. DOI: 10.25198/2077-7175-2021-3-61.

27. Boyarshinov, M. G., Vavilin, A. S., Shumkov, A. G. Fourier analysis of the traffic flow intensity. Intellekt. Innovacii. Investicii, 2021, Iss. 4, pp. 46–59. DOI: 10.25198/2077-7175-2021-4-46.

28. Boyarshinov, M. G., Vavilin, A. S., Vaskina, E. V. Application of the Hurst index to research the traffic flow intensity. Intellekt. Innovacii. Investicii, 2022, Iss. 2, pp. 68– 81. DOI: 10.25198/2077-7175-2021-2-68.

29. Boyarshinov, M. G., Vavilin, A. S., Vaskina, E. V. Application of wavelet analysis to investigate traffic flow intensity. Intellekt. Innovacii. Investicii, 2022, Iss. 4, pp. 72– 87. DOI: doi.org/10.25198/2077-7175-2022-4-72.

30. Grassberger, P., Procaccia, I. Characterization of Strange Attractors, Physical Review Letters, 1983, 50, pp. 346–349. [Electronic resource]: https://e-l.unifi.it/pluginfile.php/591014/mod_resource/content/0/PhysRevLett.50.346.pdf. Last accessed 16.01.2024.

31. Krylova, O. I., Tsvetkov, I. V. A software package and algorithm for calculating the fractal dimension and linear trend of time series. Software products and systems, 2012, Iss. 4, pp. 106–110. [Electronic resource]: https://swsys.ru/index.php?page=article&id=3320&lang. Last accessed 16.01.2024. EDN: OXSJMU.

32. Deshcherevsky, A. V. Fractal dimension, Hurst exponent and slope angle of the time series spectrum. Moscow, Institute of Seismology, Joint institute of Physics of the Earth named after O.Yu. Schmidt, Russian Academy of Sciences, 1997, 34 p. [Electronic resource]: https://www.elibrary.ru/download/elibrary_26650013_92379281.pdf. Last accessed 16.01.2024. EDN: WLESYZ.

33. Starchenko, N. V. Fractality index and local analysis of chaotic time series. Abstract of Ph.D. (Physics and Mathematics) thesis [Indeks fraktalnosti i lokalniy analiz khaoticheskikh vremennykh ryadov. Avtoref. diss…kand. fizmat nauk]. Moscow, MEPhI (State University), 2005, 24 p. [Electronic resource]: https://viewer.rusneb.ru/ru/000200_000018_RU_NLR_bibl_1085207?page=1&rotate=0&theme=white. Last accessed 16.01.2024.

34. Anisimov, I. A., Osipov, G. S. Comparison of classical and modified methods for calculating the fractal dimension of time series using the Hurst exponent [Sravnenie klassicheskogo i modifitsirovannogo metodov rascheta fraktalnoi razmernosti vremennykh ryadov s pomoshchyu pokazatelya Hersta]. International Journal of Humanities and Natural Sciences, 2020, Vol. 10–2 (49), pp. 6–10. DOI: 10.24411/2500-1000-2020-11104.

35. Drew, D. R.Traffic flow theory and control [Edition in Russian]. Moscow, Transport publ., 1972, 424 p.

36. Silyanov, V. V.Theory of traffic flows in road design and traffic organization [Teoriya transportnykh potokov v proektirovanii dorog i organizatsii dvizheniya]. Moscow, Transport publ., 1977, 303 p.

37. Sutcliffe, J., Hurst, S., Awadallah, A. G., Brown, E., Hamed, Kh. Harold Edwin Hurst: the Nile and Egypt, past and future. Hydrological Sciences Journal, 2016, Vol. 61, Iss. 9, pp. 1557–1570. DOI: 10.1080/02626667.2015.1019508.

38. Golub, Yu. Ya. Fractal dimensionality of time series multiplied by a number and multiplication of other time series [Izychenie fraktalnoy razmernosti vremennogo riada na chislo i umnozheniya vremennykh riadov]. Science and Business: Ways of Development, 2016, Iss. 5 (59), pp. 72–76. [Electronic resource]: https://www.elibrary.ru/download/elibrary_26384218_98248727.pdf. Last accessed 13.01.2024. EDN: WFJNCD.

39. Golub, Yu. Ya. Analytical Analysis of the Fractal Dimensionality of a Cross Rate of One Currency in Relation to Another [Analiticheskoe rassmotrenie fraktalnoi razmernosti kross-kursov odnoi valyuty po otnosheniyu k drugoi]. Science and Business: Ways of Development, 2014, Iss. 7 (37), pp. 42–45. [Electronic resource]: https://www.elibrary.ru/download/elibrary_22309341_13493325.pdf. Last accessed 14.01.2024. EDN: SUFBVT.

40. Mikhailov, V. V., Kirnosov, S. L., Gedzenko, M. O. Fractal model of processing streaming data in the task of forecasting weather conditions [Fraktalnaya model obrabotki potokovykh dannykh v zadache prognozirovaniya uslovii pogody]. Problems of ensuring safety in elimination of consequences of emergency situations, 2013, Vol. 2, Iss.1 (2), pphttps://www.elibrary.ru/contents.asp?id=34248283&selid=26293729.43–46. [Electronic resource]: https://www.elibrary.ru/download/elibrary_26293729_82151009.pdf. Last accessed 13.01.2024. EDN: WDKAMR.

41. Li Li, Zhiheng Li, Yi Zhang, Yudong Chen. AMixedFractal Traffic Flow Model Whose Hurst Exponent Appears Crossover. Fifth International Joint Conference on Computational Sciences and Optimization, Conference Publishing Service, 2012, pp. 443–447. DOI 10.1109/CSO.2012.103.

42. Kaklauskas, L., Sakalauskas, L. Study of on-line measurement of traffic self-similarity. CEJOR, 2013, Vol. 21, pp. 63–84. DOI 10.1007/s10100–011–0216–5.

43. Mehrvar, H. R., Le-Ngoc, T. Estimation of Degree of Self-Similarity for Traffic Control in Broadband Satellite Communications. Proceedings 1995 Canadian Conference on Electrical and Computer Engineering, Montreal, QC, Canada, 1995, Vol. 1, pp. 515–518. DOI: 10.1109/CCECE.1995.528187.

44. Glavatskiy, S. P. Statistical analysis of social media traffic [Statisticheskiy analiz trafika sotsialnykh setei]. Naukovi pratsi ONAZ im. O. S. Popova, 2013, Iss. 2, pp. 94–99. [Electronic resource]: https://www.elibrary.ru/item.asp?id=21597433. – Last accessed 14.08.2023.

45. Zhmurko, D. Yu., Osipov, A. K. Forecasting the development indicators of the sugar industry using fractal analysis methods [Prognozirovanie pokazatelei razvitiya sakharnoi otrasli s primeneniem metodov fraktalnogo analiza]. Bulletin of Udmurt University. Economics and Law, 2018, Vol. 28, Iss. 2, pp. 185–193. [Electronic resource]: https://www.elibrary.ru/download/elibrary_35078230_18865640.pdf. Last accessed 14.08.2023. EDN: LVBFMT.

46. Lopukhin, A. M. Application of fractal analysis methods to forecasting development indicators of coffee industry enterprises [Primenenie metodov fraktalnogo analiza k prognozirovaniyu pokazatelei razvitiya predpriyatiyi kofeinoi otrasli]. Continuum. Мathematics. Computer science. Education, 2020, Iss. 4, pp. 70–77. DOI: 10.24888/2500-1957-2020-4-70-79.

47. Shmyrin, A. M., Sedykh, I. A., Shcherbakov, A. P. Nonlinear analysis methods in investigating clinker production characteristics [Metody nelineinogo analiza pri issledovanii kharakteristik proizvodstva klinkera]. Vestnik TGU, 2014, Vol. 19, Iss. 3, pp. 923–926. [Electronic resource]: https://www.elibrary.ru/download/elibrary_21830477_25858437.pdf. Last accessed 14.08.2023. EDN: SJSQBH.

48. Can Ye, Huiyun Li, Guoqing Xu. An Early Warning Model of Traffic Accidents Based on Fractal Theory. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 2014, pp. 2280–2285. DOI: 10.1109/ITSC.2014.6958055.


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Boyarshinov M.G. Features of Using the Method of Normalised Range and Fractal Analysis in Studying the Car Traffic Flow Intensity. World of Transport and Transportation. 2024;22(3):12-21. https://doi.org/10.30932/1992-3252-2024-22-3-2

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