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Attention meets long short-term memory: A deep learning network for traffic flow forecasting

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  • Fang, Weiwei
  • Zhuo, Wenhao
  • Yan, Jingwen
  • Song, Youyi
  • Jiang, Dazhi
  • Zhou, Teng

Abstract

Accurate forecasting of future traffic flow has a wide range of applications, which is a fundamental component of intelligent transportation systems. However, timely and accurate traffic forecasting remains an open challenge due to the high nonlinearity and volatility of traffic flow data. Canonical long short-term memory (LSTM) networks are easily drawn to focus on min-to-min fluctuations rather than the long term dependencies of the traffic flow evolution. To address this issue, we propose to introduce an attention mechanism to the long short-term memory network for short-term traffic flow forecasting. The attention mechanism helps the network model to assign different weights to different inputs, focus on critical and important information, and make accurate predictions. Extensive experiments on four benchmark data sets show that the LSTM network equipped with an attention mechanism has superior performance compared with commonly used and state-of-the-art models.

Suggested Citation

  • Fang, Weiwei & Zhuo, Wenhao & Yan, Jingwen & Song, Youyi & Jiang, Dazhi & Zhou, Teng, 2022. "Attention meets long short-term memory: A deep learning network for traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    • Handle: RePEc:eee:phsmap:v:587:y:2022:i:c:s0378437121007585
    DOI: 10.1016/j.physa.2021.126485
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    References listed on IDEAS

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    1. Cai, Weihong & Yu, Ding & Wu, Ziyu & Du, Xin & Zhou, Teng, 2019. "A hybrid ensemble learning framework for basketball outcomes prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
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    4. Shiqiang Zheng & Shuangyi Zhang & Youyi Song & Zhizhe Lin & Dazhi Jiang & Teng Zhou & chuan lin, 2021. "A Noise-Immune Boosting Framework for Short-Term Traffic Flow Forecasting," Complexity, Hindawi, vol. 2021, pages 1-9, May.
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    Cited by:

    1. Sun, Xiaoyong & Chen, Fenghao & Wang, Yuchen & Lin, Xuefen & Ma, Weifeng, 2023. "Short-term traffic flow prediction model based on a shared weight gate recurrent unit neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    2. Luo, Jie & Wen, Chao & Peng, Qiyuan & Qin, Yong & Huang, Ping, 2023. "Forecasting the effect of traffic control strategies in railway systems: A hybrid machine learning method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).

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