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Highway Traffic Flow Nonlinear Character Analysis and Prediction

Author

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  • Meng Hui
  • Lin Bai
  • YanBo Li
  • QiSheng Wu

Abstract

In order to meet the highway guidance demand, this work studies the short-term traffic flow prediction method of highway. The Yu-Wu highway which is the main road in Chongqing, China, traffic flow time series is taken as the study object. It uses phase space reconstruction theory and Lyapunov exponent to analyze the nonlinear character of traffic flow. A new Volterra prediction method based on model order reduction via quadratic-linear systems (QLMOR) is applied to predict the traffic flow. Compared with Taylor-expansion-based methods, these QLMOR-reduced Volterra models retain more information of the system and more accuracy. The simulation results using this new Volterra model to predict short time traffic flow reveal that the accuracy of chaotic traffic flow prediction is enough for highway guidance and could be a new reference for intelligent highway management.

Suggested Citation

  • Meng Hui & Lin Bai & YanBo Li & QiSheng Wu, 2015. "Highway Traffic Flow Nonlinear Character Analysis and Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-7, September.
    • Handle: RePEc:hin:jnlmpe:902191
    DOI: 10.1155/2015/902191
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    Cited by:

    1. Su-qi Zhang & Kuo-Ping Lin, 2020. "Short-Term Traffic Flow Forecasting Based on Data-Driven Model," Mathematics, MDPI, vol. 8(2), pages 1-17, January.
    2. Anton Aleshkin, 2021. "The Influence of Transport Link Density on Conductivity If Junctions and/or Links Are Blocked," Mathematics, MDPI, vol. 9(11), pages 1-18, June.

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