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Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism

Author

Listed:
  • Tianhe Lan

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Xiaojing Zhang

    (Journal Editorial Department, Qingdao University of Technology, Qingdao 266520, China)

  • Dayi Qu

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Yufeng Yang

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Yicheng Chen

    (School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China)

Abstract

Traffic-flow prediction plays an important role in the construction of intelligent transportation systems (ITS). So, in order to improve the accuracy of short-term traffic flow prediction, a prediction model (GWO-attention-LSTM) based on the combination of optimized attention mechanism and long short-term memory (LSTM) is proposed. The model is based on LSTM and uses the attention mechanism to assign individual weight to the feature information extracted via LSTM. This can increase the prediction model’s focus on important information. The initial weight parameters of the attention mechanism are also optimized using the grey wolf optimizer (GWO). By simulating the hunting process of grey wolves, the GWO algorithm calculates the hunting position of the grey wolf and maps it to the initial weight parameters of the attention mechanism. In this way, the short-time traffic flow prediction model is constructed. The traffic flow data of the trunk roads in the center of Qingdao (China) are used as the research object. Multiple sets of comparison models are set up for prediction analysis. The results show that the GWO-attention-LSTM model has obvious advantages over other models. The prediction error MAE values of the GWO-attention-LSTM model decreased by 7.32% and 14.35% on average compared with the attention-LSTM model and LSTM model. It is concluded that the GWO-attention-LSTM model has better model performance and can provide effective help for traffic management control and traffic flow theory research.

Suggested Citation

  • Tianhe Lan & Xiaojing Zhang & Dayi Qu & Yufeng Yang & Yicheng Chen, 2023. "Short-Term Traffic Flow Prediction Based on the Optimization Study of Initial Weights of the Attention Mechanism," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1374-:d:1032050
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    References listed on IDEAS

    as
    1. Cai Zhao & Ruijing Liu & Bei Su & Lei Zhao & Zhiyong Han & Wen Zheng, 2022. "Traffic Flow Prediction with Attention Mechanism Based on TS-NAS," Sustainability, MDPI, vol. 14(19), pages 1-12, September.
    2. Daniela Durand & Jose Aguilar & Maria D. R-Moreno, 2022. "An Analysis of the Energy Consumption Forecasting Problem in Smart Buildings Using LSTM," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    3. Min Li & Mengshan Li & Bilong Liu & Jiang Liu & Zhen Liu & Dijia Luo, 2022. "Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    4. Hao Zhang & GuangLong Dai, 2019. "The strategy of traffic congestion management based on case-based reasoning," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(1), pages 142-147, February.
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