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Short-Term Traffic Flow Forecasting Based on Periodicity Similarity Characteristics

  • Chunjiao Dong
  • Chunfu Shao
  • Dan Zhao
  • Yinhong Liu
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 156)

Abstract

The methodology has been putted forward that the periodicity similarity should be consideration when using Elman neural network (ENN) to forecast short-term traffic flow, which is not only helpful to save training time, reduce training sample size, but also enhance forecasting efficiency. Firstly, training sample of ENN has been designed based on the periodicity similarity of traffic flow and network structure has been established aiming at improving ENN global stability. Secondly, short-term traffic flow forecasting method based on ENN have been established by taking daily, weekly and monthly periodicity similarity into account respectively. Finally, forecasting results have been evaluated by four error statistics from two aspects: forecasting effect and efficiency. The conclusion has been summarized to three aspects.

Keywords

Traffic Flow Similarity Coefficient Mean Absolute Percentage Error Forecast Result Transportation Research Part 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Chunjiao Dong
    • 1
    • 2
  • Chunfu Shao
    • 2
  • Dan Zhao
    • 2
  • Yinhong Liu
    • 2
  1. 1.Center of Transportation ResearchKnoxvilleUSA
  2. 2.MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology School of Traffic & TransportationBeijing Jiaotong UniversityBeijingChina

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