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On Application of Regime-Switching Models for Short-Term Traffic Flow Forecasting

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Advances in Dependability Engineering of Complex Systems (DepCoS-RELCOMEX 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 582))

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Abstract

This paper contributes to the identification of spatial dependency regimes in urban traffic flows. Importance of traffic flow regimes for forecasting and presence of spatial relationships between road network nodes are widely acknowledged both in traffic flow theory and empirical studies. In this research, we join these concepts and made the first steps to analysis of different regimes of spatial dependency in a traffic flow. Modern Markov-switching autoregressive distributed lag models are utilized and allowed to analyse the model structure in different traffic flow regimes. On the base of the models, we made a conclusion about the importance of traffic flow regimes for identification of a structure of spatial dependencies. The proposed approach is illustrated for real-world traffic flow data.

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References

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

    Google Scholar 

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

    Article  Google Scholar 

  3. Min, X., Hu, J., Chen, Q., Zhang, T., Zhang, Y.: Short-term traffic flow forecasting of urban network based on dynamic STARIMA model. In: 2009 12th International IEEE Conference on Intelligent Transportation Systems, pp. 1–6 (2009)

    Google Scholar 

  4. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transp. Res. Part C: Emerg. Technol. 43, 3–19 (2014)

    Article  Google Scholar 

  5. Williams, B.M.: “Real-time road traffic forecasting using regime-switching space-time models and adaptive lasso” by Y. Kamarianakis, W. Shen, and L. Wynter. Appl. Stoch. Models Bus. Ind. 28, 319–321 (2012)

    Article  MathSciNet  Google Scholar 

  6. Ermagun, A.: Network Econometrics and Traffic Flow Analysis (2016). http://conservancy.umn.edu/handle/11299/183378

  7. Yu, G., Zhang, C.: Switching ARIMA model based forecasting for traffic flow. In: 2004 Proceedings on Acoustics, Speech, and Signal Processing, vol. 2, pp. 429–432. IEEE (2004)

    Google Scholar 

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

    Article  Google Scholar 

  9. Kamarianakis, Y., Shen, W., Wynter, L.: Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO. Appl. Stochast. Models Bus. Ind. 28, 297–315 (2012)

    Article  MathSciNet  Google Scholar 

  10. Kamarianakis, Y., Prastacos, P.: Space–time modeling of traffic flow. Comput. Geosci. 31, 119–133 (2005)

    Article  Google Scholar 

  11. Li, L., Su, X., Wang, Y., Lin, Y., Li, Z., Li, Y.: Robust causal dependence mining in big data network and its application to traffic flow predictions. Transp. Res. Part C: Emerg. Technol. 58, 292–307 (2015)

    Article  Google Scholar 

  12. Greenshields, B.D.: A study of traffic capacity. In: Proceedings of the Highway Research Board, pp. 448–477 (1935)

    Google Scholar 

  13. Sun, L., Zhou, J.: Development of multiregime speed-density relationships by cluster analysis. Transp. Res. Rec.: J. Transp. Res. Board 1934, 64–71 (2005)

    Article  Google Scholar 

  14. Yang, S., Shi, S., Hu, X., Wang, M.: Spatiotemporal context awareness for urban traffic modeling and prediction: sparse representation based variable selection. PLoS ONE 10, e0141223 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Transport and Telecommunication Institute, Lomonosova 1, Riga, 1019, Latvia

    Dmitry Pavlyuk

Authors
  1. Dmitry Pavlyuk
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Correspondence to Dmitry Pavlyuk .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Wrocław University of Technology Department of Computer Engineering, Wrocław, Poland

    Wojciech Zamojski

  2. Department of Computer Engineering, Wrocław University of Technology Department of Computer Engineering, Wrocław, Poland

    Jacek Mazurkiewicz

  3. Department of Computer Engineering, Wrocław University of Technology, Wroclaw, Poland

    Jarosław Sugier

  4. Department of Computer Engineering, Wrocław University of Technology Department of Computer Engineering, Wrocław, Poland

    Tomasz Walkowiak

  5. Systems Research Institute, Polish Academy of Sciences Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Pavlyuk, D. (2018). On Application of Regime-Switching Models for Short-Term Traffic Flow Forecasting. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Advances in Dependability Engineering of Complex Systems. DepCoS-RELCOMEX 2017. Advances in Intelligent Systems and Computing, vol 582. Springer, Cham. https://doi.org/10.1007/978-3-319-59415-6_33

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  • DOI: https://doi.org/10.1007/978-3-319-59415-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59414-9

  • Online ISBN: 978-3-319-59415-6

  • eBook Packages: EngineeringEngineering (R0)

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