Skip to main content
SpringerLink
Log in

Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

Adverse weather conditions may have a significant impact on the statistical characteristics of traffic variables such as volume and speed, and consequently on their predictability. We address the problem of freeway lane speed variability under the effect of precipitation episodes and present a non-linear dynamics methodology based on recurrence quantification analysis to compare speed evolution under fine and adverse weather conditions. Findings indicate that, under fine weather conditions, section travel speed is an adequate descriptor of traffic evolution in the left and middle lanes for high demand, but fails in cases of incidents or low traffic. Moreover, when it rains, regardless of intensity and duration, a significantly dissimilar evolution of lane speed is observed; this difference should be considered in the process of short-term traffic forecasting.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Lin, Q., Nixon, W.: Effects of adverse weather on traffic crashes: systematic review and meta-analysis. In: Proceedings of the 87nd Annual Meeting of the Transportation Research Board (CD ROM), Transportation Research Board of the National Academies, Washington, D.C. (2008)

    Google Scholar 

  2. FHWA Report No. FHWA-HOP-06-090, 28 February (2006). Integration of Emergency and Weather Elements into Transportation Management Centers

  3. Hall, F.L., Ibrahim, A.T.: Effect of adverse weather condition on speed-occupancy relationships. Transp. Res. Rec. 1457, 184–191 (1994)

    Google Scholar 

  4. Edwards, J.B.: Speed adjustment of motorway commuter traffic to inclement weather. Transp. Res., Part F 2(1), 1–14 (1999)

    Article  Google Scholar 

  5. Carter, M., Rakha, H., Van Aerde, M.: Variability of traffic-flow measures across freeway lanes. Can. J. Civ. Eng. 26(3), 270–281 (1999)

    Article  Google Scholar 

  6. Kyte, M., Khatib, Z., Shannon, P., Kitchener, F.: The effect of weather on free flow speed. Transp. Res. Rec. 1776, 60–68 (2001)

    Article  Google Scholar 

  7. Keay, K., Simmonds, I.: The association of rainfall and other weather variables with road traffic volume in Melbourne, Australia. Accid. Anal. Prev. 37, 109–124 (2005)

    Article  Google Scholar 

  8. Rakha, H. Farzaneh M., Arafeh, M., Hranac, R., Sterzin, E., Krechmer, D.: Empirical studies on traffic flow in inclement weather. Final Report—Phase I, FHWA-HOP-07-073, Washington, D.C. (2007)

  9. Prevedouros, P.D., Kongsil, P.: Synthesis of the Effects of Wet Conditions on Highway Speed and Capacity, 21 July, 2003, Honolulu, HI (2003)

    Google Scholar 

  10. Kockelman, M.K., Ma, J.: Freeway speeds and speed variations preceding crashes, within and across lanes. J. Transp. Res. Forum 46(1), 43–61 (2007)

    Google Scholar 

  11. Oh, J.S., Shim, Y.U., Cho, Y.H.: Effect of weather conditions to traffic flow on freeway. KSCE J. Civ. Eng. 6(4), 413–420 (2002)

    Article  Google Scholar 

  12. Florio, L., Mussone, F.: Neural network models for classification and forecasting of highway traffic flow stability. Control Eng. Pract. 4(2), 153–164 (1996)

    Article  Google Scholar 

  13. Huang, S.H., Ran, B.: An application of neural network on traffic speed prediction under adverse weather conditions. In: Proc. Transportation Research Board 2003 A. Meet, Transportation Research Board, Washingtion D.C. (2003)

    Google Scholar 

  14. Dailey, D.: The use of weather data to predict non-recurring traffic congestion. Technical Report No. WA-RD 655.1, Washington, D.C. (2006)

  15. Vlahogianni, E., Karlaftis, M., Kopelias, P.: Modeling freeway travel speed across lanes: A vector autoregressive approach. In: The 13th International IEEE Conference on Intelligent Transportation Systems, Madeira Island, Portugal, 19–22 September (2010)

    Google Scholar 

  16. Tsirigotis, L., Vlahogianni, E.I., Karlaftis, M.G.: Does information on weather affect the performance of short-term traffic forecasting models? Int. J. Transp. Res. 10(1), 1–10 (2012)

    Google Scholar 

  17. Ahmed, M.S., Cook, A.R.: Analysis of freeway traffic time-series data by using Box–Jenkins techniques. Transp. Res. Board 722, 1–9 (1979)

    Google Scholar 

  18. Levin, M., Tsao, Y.-D.: On forecasting freeway occupancies and volumes. Transp. Res. Rec. 773, 47–49 (1980)

    Google Scholar 

  19. Davis, G.A., Niham, N.L., Hamed, M.M., Jacobson, L.N.: Adaptive forecasting of freeway traffic congestion. Transp. Res. Rec. 1287, 29–33 (1991)

    Google Scholar 

  20. Hamed, M.M., Al-Masaeid, H.R., Bani Said, Z.M.: Short-term prediction of traffic volume in urban arterials. ASCE J. Transp. Eng. 121(3), 249–254 (1995)

    Article  Google Scholar 

  21. Williams, B.M., Durvasula, P.K., Brown, D.E.: Urban traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transp. Res. Rec. 1644, 132–144 (1998)

    Article  Google Scholar 

  22. Williams, B.M.: Multivariate vehicular traffic flow prediction: an evaluation of ARIMAX modeling. Transp. Res. Rec. 1776, 194–200 (2001)

    Article  Google Scholar 

  23. Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. ASCE J. Transp. Eng. 129(6), 664–672 (2003)

    Article  Google Scholar 

  24. Stathopoulos, A., Karlaftis, M.G.: A multivariate state-space approach for urban traffic flow modeling and prediction. Transp. Res., Part C 11(2), 121–135 (2003)

    Article  Google Scholar 

  25. Kamarianakis, Y., Prastacos, P.: Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transp. Res. Rec. 1857, 74–84 (2003)

    Article  Google Scholar 

  26. Kamarianakis, Y., Kanas, A., Prastacos, P.: Modeling traffic flow volatility dynamics in an urban network. Transp. Res. Rec. 1923, 18–27 (2005)

    Article  Google Scholar 

  27. Karlaftis, M.G., Vlahogianni, E.I.: Memory properties and fractional integration in transportation time-series. Transp. Res., Part C 17(4), 444–453 (2009)

    Article  Google Scholar 

  28. Cetin, C., Comert, G.: Short-term traffic flow prediction with regime switching models. Transp. Res. Rec. 1965, 23–31 (2006)

    Article  Google Scholar 

  29. Kamarianakis, Y., Gao, H.O., Prastacos, P.: Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions. Transp. Res., Part C Emerg. Technol. 18(5), 821–840 (2010). Applications of Advanced Technologies in Transportation: Selected papers from the 10th AATT Conference

    Article  Google Scholar 

  30. Nicholson, H., Swann, C.D.: The prediction of traffic flow volumes based on spectral analysis. Transp. Res. 8(6), 533–538 (1974)

    Article  Google Scholar 

  31. Stathopoulos, A., Karlaftis, M.G.: Spectral and cross-spectral analysis of urban traffic flows. In: IEEE Proceedings, Conference on Intelligent Transportation Systems, ITSC, Oakland, CA, USA, pp. 820–825 (2001)

    Google Scholar 

  32. Samant, A., Adeli, H.: Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis. Comput.-Aided Civ. Infrastruct. Eng. 15(4), 241–250 (2000)

    Article  Google Scholar 

  33. Karim, A., Adeli, H.: Fast automatic incident detection on urban and rural freeways using wavelet energy algorithm. J. Transp. Eng. 129(1), 57–68 (2003)

    Article  Google Scholar 

  34. Karim, A., Adeli, H.: Incident detection algorithm using wavelet energy representation of traffic patterns. J. Transp. Eng. 128(3), 232–242 (2002)

    Article  Google Scholar 

  35. Jiang, X., Adeli, H.: Wavelet packet-autocorrelation function method for traffic flow pattern analysis. Comput.-Aided Civ. Infrastruct. Eng. 19(5), 324–337 (2004)

    Article  Google Scholar 

  36. Ghosh-Dastidar, S., Adeli, H.: Neural network-wavelet microsimulation model for delay and queue length estimation at freeway work zones. J. Transp. Eng. 132(4), 331–341 (2006)

    Article  Google Scholar 

  37. Vlahogianni, E.I., Geroliminis, N., Skabardonis, A.: Empirical and analytical investigation of traffic flow regimes and transitions in signalized arterials. ASCE J. Transp. Eng. 134, 512–522 (2008)

    Article  Google Scholar 

  38. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume. Transp. Res., Part C Emerg. Technol. 14(5), 351–367 (2006)

    Article  Google Scholar 

  39. Xu, N., Shang, P., Kamae, S.: Modeling traffic flow correlation using DFA and DCCA. Nonlinear Dyn. 61, 207–216 (2010)

    Article  MATH  Google Scholar 

  40. Vlahogianni, E.I., Webber, Ch.L. Jr., Geroliminis, N., Skabardonis, A.: Statistical characteristics of transitional queue conditions in signalized arterials. Transp. Res., Part C Emerg. Technol. 15(6), 392–403 (2007)

    Article  Google Scholar 

  41. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Temporal evolution of short-term urban traffic flow: A non-linear dynamics approach. Comput.-Aided Civ. Infrastruct. Eng. 23(7), 536–548 (2008)

    Article  Google Scholar 

  42. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C., Kourbelis, N.D.: Pattern-based short-term urban traffic predictor. In: Proceedings of the 9th International IEEE Conference on Intelligent Transportation Systems, September, Torondo, Canada (2006)

    Google Scholar 

  43. Vlahogianni, E.I.: Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. J. Intell. Transport. Syst. 13(2), 73–84 (2009)

    Article  Google Scholar 

  44. Diks, C., van Zwet, W.R., Takens, F., DeGoede, J.: Detecting differences between delay vector distributions. Phys. Rev. E 53, 2169 (1996)

    Article  MathSciNet  Google Scholar 

  45. Marwan, N., Romano, M.C., Thiel, M., Kurths, J.: Phys. Rep. 438, 237 (2007)

    Article  MathSciNet  Google Scholar 

  46. Zbilut, J.P., Webber, C.L. Jr.: Embeddings and delays as derived from quantification of recurrence plots. Phys. Lett. A 171, 199–203 (1992)

    Article  Google Scholar 

  47. Webber, C.L. Jr., Zbilut, J.P.: Dynamical assessment of physiological systems and states using recurrence plot strategies. J. Appl. Physiol. 76(2), 965–973 (1994)

    Google Scholar 

  48. Casdagli, M.C.: Recurrence plots revisited. Physica D, Nonlinear Phenom. 108(1–2), 12–44 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  49. Webber, C.L. Jr., Zbilut, J.P.: Recurrence quantification analysis of nonlinear dynamical systems. In: Riley, M.A., Van Orden, G. (eds.) Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences (Chap. 2), pp. 26–94 (2005). http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.pdf

    Google Scholar 

  50. Schinkel, S., Marwan, N., Dimigen, O., Kurths, J.: Confidence bounds of recurrence-based complexity measures. Phys. Lett. A 373(2009), 2245–2250 (2009)

    Article  Google Scholar 

  51. Attica Tollway. http://www.aodos.gr/ (2010). Accessed January 2010

  52. Meteonet. http://meteonet.chi.civil.ntua.gr/wk/main/splash (2010). Accessed January 2010

  53. Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  54. Kennel, M.B., Brown, R., Abarbanel, H.D.I.: Determining embedding dimension for phasespace reconstruction using a geometrical construction. Phys. Rev. A 45, 3403 (1992)

    Article  Google Scholar 

  55. Kerner, B.S.: Three-phase traffic theory and highway capacity. Physica A 333, 379–440 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is a part of a Basic Research program funded by National Technical University of Athens State Resources. The data are a courtesy of Attica Tollway Operations Authority. Analyses are conducted using the Cross Recurrence Plot Toolbox 5.16 in Matlab (http://tocsy.pik-potsdam.de/CRPtoolbox/).

Author information

Authors and Affiliations

  1. Department of Transportation Planning and Engineering, School of Civil Engineering, National Technical University of Athens, 5 Iroon Polytechniou Str, Athens, Attiki, 157 73, Greece

    Eleni I. Vlahogianni & Matthew G. Karlaftis

Authors
  1. Eleni I. Vlahogianni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew G. Karlaftis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eleni I. Vlahogianni.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vlahogianni, E.I., Karlaftis, M.G. Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures. Nonlinear Dyn 69, 1949–1963 (2012). https://doi.org/10.1007/s11071-012-0399-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-012-0399-x

Keywords

Search

Navigation