Long-Term Traffic Flow Prediction for Highways Based on STLLformer Model

Yonggang Shen, Lu Wang, Yuting Zeng, Zhumei Gou, Chengquan Wang and Zhenwei Yu ()
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Yonggang Shen: Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China
Lu Wang: Polytechnic Institute , Zhejiang University, Hangzhou 310015, China
Yuting Zeng: Polytechnic Institute , Zhejiang University, Hangzhou 310015, China
Zhumei Gou: Polytechnic Institute , Zhejiang University, Hangzhou 310015, China
Chengquan Wang: School of Engineering, Hangzhou City University, Hangzhou 310023, China
Zhenwei Yu: College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

Sustainability, 2025, vol. 17, issue 22, 1-18

Abstract: Long-term traffic flow prediction (LTFP) is crucial for intelligent transportation systems but remains challenging due to complex spatiotemporal dependencies and multi-scale temporal patterns. While recent models like Autoformer have introduced decomposition techniques, they often lack tailored mechanisms for traffic data′s unique characteristics, such as strong periodicity and long-range spatial correlations. To address this gap, we propose STLLformer, a novel spatiotemporal Transformer that establishes a seasonal-dominated, multi-component collaborative forecasting paradigm. Unlike existing approaches that merely combine decomposition with graph networks, STLLformer features: (1) a dedicated encoder–decoder architecture for separate yet synergistic modeling of trend, seasonal, and residual components; (2) a seasonal-driven autocorrelation mechanism that purely captures cyclical patterns by filtering out trend and noise interference; and (3) a low-rank graph convolutional module specifically designed to capture dynamic, long-range spatial dependencies in road networks. Experiments on two real-world traffic datasets (PEMSD8 and HHY) demonstrate that STLLformer outperforms strong baseline methods (including LSTGCN, LSTM, and ARIMA), achieving an average improvement of over 10% in MAE and RMSE (e.g., on PEMSD8 for 6-h prediction, MAE drops from 36.87 to 30.34), with statistical significance ( p < 0.01). This work provides a more refined and effective decomposition-fusion solution for traffic forecasting, which holds practical promise for enhancing urban traffic management and alleviating congestion.

Keywords: STL decomposition; long-term prediction; spatiotemporal; graph convolution; low-rank (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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