4.6 Article

Self-attention eidetic 3D-LSTM: Video prediction models for traffic flow forecasting

Journal

NEUROCOMPUTING
Volume 509, Issue -, Pages 167-176

Publisher

ELSEVIER
DOI: 10.1016/j.neucom.2022.08.060

Keywords

Video prediction; Traffic flow forecasting; Deep learning; Self -attention mechanism; Long short-term memory (LSTM) network

Funding

  1. Guanghua Talent Project of South- western University of Finance and Economics
  2. National Natural Science Foundation of China [72171196]

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This study proposes a video prediction model using a query-key-value self-attention mechanism in traffic flow forecasting. The model optimizes the long-term relationship by introducing self-attention mechanism from both algorithm and network architecture perspectives.
Video prediction is extremely challenging in a traffic flow forecasting problem due to dynamic spatiotem-poral dependence. Eidetic 3D convolutional long short-term memory (E3D-LSTM) network, a state-of-the-art video prediction model, proposes a gate-controlled self-attention module called recall gate in the LSTM mechanism to make the present memory state interact with its historical records for long-term relations. Instead of using the gate-controlled self-attention mechanism, we introduce a query -key-value self-attention mechanism into E3D-LSTM for long-term relations from the perspectives of algo-rithm (internal) and network architecture (external). As for the algorithm (internal) perspective, we replace the original recall gate inside the E3D-LSTM cell with a query-key-value self-attention (SA) mod-ule. While for the network architecture (external) perspective, we propose an independent residual query-key-value self-attention (RSA) block outside E3D-LSTM networks in conjunction with the original recall gate. In a traffic flow forecasting problem, we find that both the models from the internal perspec-tive and the external one, named SAE3D-LSTM and RSA-E3D-LSTM, outperform E3D-LSTM and seven other baseline models on three traffic datasets. This validates the effectiveness of the query-key-value self-attention mechanism for long-term relations. Furthermore, we find that SAE3D-LSTM performs bet-ter than RSA-E3D-LSTM. This indicates that the query-key-value self-attention mechanism alone can cap-ture long-term relations, dispensing with the gate-controlled self-attention mechanism.(c) 2022 Elsevier B.V. All rights reserved.

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