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Research on Short-Term Traffic Flow Forecasting Based on KNN and Discrete Event Simulation

  • Shaozheng YuEmail author
  • Yingqiu Li
  • Guojun Sheng
  • Jiao Lv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

With the rapid development of urban traffic, it is very important to achieve accurate short-term traffic flow forecasting. Firstly, with the problem of short-term traffic flow forecasting, the key features that affect the traffic flow are extracted and the KNN non-parametric regression method is used for forecasting. Secondly, in order to solve the problem of dynamic traffic flow assignment, we build a simulation model and achieved good results. Finally, we use the case of short-term flow forecasting in airport to carry out a data experiment. The experimental results show that the traffic flow of traffic nodes and routes can be forecasted completely by using KNN algorithm combined with discrete event simulation technology, and the results are more credible.

Keywords

Short-term traffic flow forecasting Nonparametric regression K nearest neighbor Discrete event simulation 

References

  1. 1.
    Chuan, L.: Short-term traffic flow forecasting algorithm based on K-nearest Neighbor Nonparametric Regression (2015)Google Scholar
  2. 2.
    Yakowitz, S.: Nearest-neighbor methods for time series analysis. J. Time Ser. Anal. 8(2), 10–26 (1987)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Davis, G., Nihan, N.: Nonparametric regression and short-term freeway traffic forecasting. J. Transp. Eng. 117(2), 178–188 (1991)CrossRefGoogle Scholar
  4. 4.
    Smith, B.L., Demetsky, M.J.: Traffic flow forecasting: comparison of modeling approaches. J. Transp. Eng. 123(4), 261–266 (1997)CrossRefGoogle Scholar
  5. 5.
    Smith, B.L., Williams, B.M.: Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C 10, 303–321 (2002)CrossRefGoogle Scholar
  6. 6.
    Clark, S.: Traffic forecasting using multivariate nonparametric regression. J. Transp. Eng. 129(2), 161–168 (2003)CrossRefGoogle Scholar
  7. 7.
    Turochy, R.E.: Enhancing short-term traffic forecasting with traffic condition information. J. Transp. Eng. 132(6), 469–474 (2006)CrossRefGoogle Scholar
  8. 8.
    Kindzerske, M.D., Ni, D.H.: Composite nearest neighbor nonparametric regression to improve traffic forecasting. J. Transp. Res. Board 1993, 30–35 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Shaozheng Yu
    • 1
    Email author
  • Yingqiu Li
    • 1
  • Guojun Sheng
    • 1
  • Jiao Lv
    • 1
  1. 1.Dalian Neusoft University of InformationDalianChina

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