Short-term traffic flow prediction model based on GWO-attention-LSTM

Tianhe Lan; Dayi Qu; Kun Chen; Haomin Liu

doi:10.1117/12.2686538

10 August 2023 Short-term traffic flow prediction model based on GWO-attention-LSTM

Tianhe Lan, Dayi Qu, Kun Chen, Haomin Liu

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127592Y (2023) https://doi.org/10.1117/12.2686538
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

Accurate traffic flow prediction results can help to reduce traffic congestion, optimize travel routes and improve road capacity, etc. Therefore, a short-term traffic flow prediction model based on GWO-Attention-LSTM is proposed, which can deeply explore the single-factor information features of traffic flow. The model is based on Long Short-Term Memory (LSTM), and Attention mechanism is added to enhance the focus on important information. The initial weight parameter values of Attention mechanism are optimized by Grey Wolf Optimizer (GWO), so as to mine and predict the time series of traffic flow. The traffic flow data of the actual road is simulated and analyzed. The results show that the RMSE values of GWO-Attention-LSTM model decrease by 1% and 2% compared with Attention-LSTM model and LSTM model, respectively. It proves that GWO-Attention-LSTM model has lower prediction error and better model performance by adding Attention mechanism and GWO algorithm.

Citation Download Citation

Tianhe Lan, Dayi Qu, Kun Chen, and Haomin Liu "Short-term traffic flow prediction model based on GWO-attention-LSTM", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127592Y (10 August 2023); https://doi.org/10.1117/12.2686538

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