Skip to main content
Top
Published in: International Journal of Intelligent Transportation Systems Research 1/2019

16-01-2018

Fractal Analysis of the Relation between the Observation Scale and the Prediction Cycle in Short-Term Traffic Flow Prediction

Authors: Sheng Zhang, Zhong-xiang Huang

Published in: International Journal of Intelligent Transportation Systems Research | Issue 1/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Based on the analysis of the field traffic flow time series, we found that there is self-similarity and periodic similarity in the traffic flow of different observation scales, which makes the short-term traffic flow prediction a meaningful work. For the purpose of finding the smallest prediction cycle, fractal analysis was conducted in the relation between the observation scale and the prediction cycle by using both the field data and the simulated data. We calculate the fractal dimension and the scaling region of traffic flow time series by using the G-P algorithm. If the scaling region can be found in the traffic flow time series at some observation scale, it means that there is self-similarity in the time series at that observation scale. The minimum observation scale at which there is self-similarity in the traffic flow is the smallest prediction cycle. This observation scale is a prerequisite for judging whether the traffic flow can be predicted or not. This research provides a reference for the short-term traffic flow prediction on expressway and urban road.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information. 

Order your 30-days-trial for free and without any commitment.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Show more products
Literature
1.
go back to reference Okutani, I., Stephamedes, Y.J.: Dynamic prediction of traffic volume through Kalman filtering theory. Transp. Res. B. 18B(1), 1–11 (1984)CrossRef Okutani, I., Stephamedes, Y.J.: Dynamic prediction of traffic volume through Kalman filtering theory. Transp. Res. B. 18B(1), 1–11 (1984)CrossRef
3.
go back to reference Smith, B.L., Demetsky, M.J.: Short-term traffic flow prediction: neural network approach. Transp. Res. Rec. 1453, 98–104 (1994) Smith, B.L., Demetsky, M.J.: Short-term traffic flow prediction: neural network approach. Transp. Res. Rec. 1453, 98–104 (1994)
6.
go back to reference May, A.D.: Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs (1990) May, A.D.: Traffic Flow Fundamentals. Prentice Hall, Englewood Cliffs (1990)
7.
go back to reference van Zuylen H.J., van Geenhuizen M.S., P. Nijkamp. (Un)predictability in Traffic and Transport Decision Making, vol 1685. Transportation research record, TRB, National Research Council, Washington DC pp. 21–28, 1999 van Zuylen H.J., van Geenhuizen M.S., P. Nijkamp. (Un)predictability in Traffic and Transport Decision Making, vol 1685. Transportation research record, TRB, National Research Council, Washington DC pp. 21–28, 1999
8.
go back to reference Heinz-Otto Peitgen, H. Jürgens, Saupe, D.: Chaos and Fractals: New Frontiers of Science. Springer-Verlag, New York (1992) Heinz-Otto Peitgen, H. Jürgens, Saupe, D.: Chaos and Fractals: New Frontiers of Science. Springer-Verlag, New York (1992)
10.
go back to reference Cheng, X.J., Liu, J., Ma, M.S.: Algorithm of short-term traffic flow prediction based on fractal theory. J. Transp. Syst. Eng. Inf. Technol. 10(4), 106–110 (2010) Cheng, X.J., Liu, J., Ma, M.S.: Algorithm of short-term traffic flow prediction based on fractal theory. J. Transp. Syst. Eng. Inf. Technol. 10(4), 106–110 (2010)
11.
go back to reference Feng, W.D., Chen, J., He, G.G.: Study of the fractal phenomenon in traffic flow. High Technol. Lett. 13(6), 59–65 (2003) Feng, W.D., Chen, J., He, G.G.: Study of the fractal phenomenon in traffic flow. High Technol. Lett. 13(6), 59–65 (2003)
12.
go back to reference Zhang, Y., Guan, W.: Empirical research of the fractal in the traffic flow time series. Journal of highway and transportation research and development. 27(5), 100–103 (2010) Zhang, Y., Guan, W.: Empirical research of the fractal in the traffic flow time series. Journal of highway and transportation research and development. 27(5), 100–103 (2010)
13.
go back to reference Whitney, H. Differentiable Manifolds. Ann. Math 37:645–680, 1936 Whitney, H. Differentiable Manifolds. Ann. Math 37:645–680, 1936
14.
go back to reference Takens, F.: Detecting strange attractors in turbulence. Lect. Notes in Math. 1936, 898 (1981)MATH Takens, F.: Detecting strange attractors in turbulence. Lect. Notes in Math. 1936, 898 (1981)MATH
Metadata
Title
Fractal Analysis of the Relation between the Observation Scale and the Prediction Cycle in Short-Term Traffic Flow Prediction
Authors
Sheng Zhang
Zhong-xiang Huang
Publication date
16-01-2018
Publisher
Springer US
Published in
International Journal of Intelligent Transportation Systems Research / Issue 1/2019
Print ISSN: 1348-8503
Electronic ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-017-0151-5

Other articles of this Issue 1/2019

International Journal of Intelligent Transportation Systems Research 1/2019 Go to the issue

Premium Partners