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Short-Term Traffic Flow Forecasting Based on Data-Driven Model

Author

Listed:
  • Su-qi Zhang

    (Department of computer science, Tianjin University of Commerce, Tianjin 300134, China)

  • Kuo-Ping Lin

    (Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan)

Abstract

Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:152-:d:311641
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    References listed on IDEAS

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    1. Cheng, Anyu & Jiang, Xiao & Li, Yongfu & Zhang, Chao & Zhu, Hao, 2017. "Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 422-434.
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    3. 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.
    4. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
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    Cited by:

    1. Xing Wan & Xing-Quan Zuo & Xin-Chao Zhao, 2021. "A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem," Mathematics, MDPI, vol. 9(15), pages 1-18, July.

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