Skip to main content
SpringerLink
Log in

Real-time travel-time prediction method applying multiple traffic observations

  • Transportation Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope Submit manuscript

Abstract

Various methods have been developed to predict automobile travel time, but they are often unreliable, especially when the travel time varies significantly during the transition between free flow and congested flow. This paper proposes a real-time travel-time prediction method. We apply a macroscopic traffic flow model with predicted boundary conditions and modify the scheme to calculate the traffic states to reflect the latest traffic conditions on a real-time basis. Our method uses traffic data from multiple observation systems, which is a crucial component for real-time application of the macroscopic traffic flow model that has not been previously applied to traffic flow models. The analysis of real traffic data collected from a section of the Korean Kyungbu Expressway shows that the proposed method outperforms other prediction methods, particularly during the transition between free flow and congested flow.

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

Similar content being viewed by others

References

  • Chen, M. and Chien, S. I. J. (2000). “Determining the number of probe vehicles for freeway travel time estimation by microscopic simulation.” Transportation Research Record: Journal of the Transportation Research Board, No. 1719, pp. 61–68, DOI: 10.3141/1719-08.

    Article  Google Scholar 

  • Choi, K. (1999). “Data fusion methodology for link travel time estimation for advanced traveler information system.” KSCE Journal of Civil Engineering, Vol. 3, Issue 1, pp. 1–14, DOI: 10.1007/BF02830731.

    Article  Google Scholar 

  • Coifman, B. (2002). “Estimating travel times and vehicle trajectories on freeways using dual loop detectors.” Transportation Research Part A: Policy and Practice, Vol. 36, Issue 4, pp. 351–364, DOI: 10.1016/S0965-8564(01)00007-6.

    Google Scholar 

  • Du, L., Peeta, S., and Kim, Y. H. (2012). “An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks.” Transportation Research Part B: Methodological, Vol. 46, Issue 1, pp. 235–252, DOI: 10.1016/j.trb.2011.09.008.

    Article  Google Scholar 

  • El Esawey, M. and Sayed, T. A. (2009). “Travel time estimation in an urban network using sparse probe vehicle data and historical travel time relationships.” Transportation Research Board 88th Annual Meeting, Washington D.C.

    Google Scholar 

  • Faouzi, N-E. E., Leung, H., and Kurian, A. (2011). “Data fusion in intelligent transportation systems: Progress and challenges -A survey.” Information Fusion, Vol. 12, Issue 1, pp. 4–10, DOI: 10.1016/j.inffus.2010.06.001.

    Article  Google Scholar 

  • Lim, S. and Lee, C. (2011). “Data fusion algorithm improves travel time predictions.” IET Intell. Transp. Syst., Vol. 5, Issue 4, 2011, pp. 302–309, DOI: 10.1049/iet-its.2011.0014.

    Article  Google Scholar 

  • Oh, C. and Park, S. (2011). “Investigating the effects of daily travel time patterns on short-term prediction.” KSCE Journal of Civil Engineering, Vol. 15, Issue 7, DOI: 10.1007/s12205-011-1123-y.

  • Papageorgiou, M., Blosseville, J. M., and Hadj-Salem, H. (1990). “Modelling and real-time control of traffic flow on the southern part of boulevard Périphérique in Paris: Part I: Modelling.” Transportation Research Part A: General, Vol. 24, Issue 5, pp. 345–359, DOI: 10.1016/0191-2607(90)90047-A.

    Article  Google Scholar 

  • Payne, H. J. (1971). Models of freeway traffic and control, Simulation councils, Inc., pp. 51–61.

    Google Scholar 

  • Tam, M. L. and Lam, W. H. K. (2009). “Short-term travel time prediction for congested urban road Networks.” Transportation Research Board 88th Annual Meeting, Washington D.C.

    Google Scholar 

  • Van Lint, J. W. C. (2004). Reliable travel time prediction for freeways, Trail Thesis Series, No. T2004/3, The Netherlands TRAIL Research School, Delft, Netherland.

    Google Scholar 

  • Van Lint, J. W. C., Hoogendoorn, S. P., and Van Zuylen, H. J. (2005). “Accurate freeway travel time prediction with state-space neural networks under missing data.” Transportation Research Part C: Emerging Technologies, Vol. 13, Issues 5-6, pp. 347–369, DOI: 10.1016/j.trc.2005.03.001.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Convergence Research Planning Division, Korea Institute of Civil Engineering and Building Technology, Goyang, 10223, Korea

    Sung Han Lim

  2. Dept. of Road Transport Research, The Korea Transport Institute, Sejong, 30147, Korea

    Youngho Kim

  3. Dept. of Civil and Environmental Engineering, Seoul National University, Seoul, 08826, Korea

    Chungwon Lee

Authors
  1. Sung Han Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Youngho Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chungwon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chungwon Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lim, S.H., Kim, Y. & Lee, C. Real-time travel-time prediction method applying multiple traffic observations. KSCE J Civ Eng 20, 2920–2927 (2016). https://doi.org/10.1007/s12205-016-0239-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-016-0239-5

Keywords

Search

Navigation