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STIL-TA: A new model of traffic flow forecasting based on spatiotemporal interactive learning and temporal attention
Linlong Chen1, Linbiao Chen2, Hongyan Wang3
1School of Big Data & Information Engineering, Guiyang Institute of Humanities and Technology, Guiyang, China.
Plos One
|August 25, 2025
Summary
Accurate traffic flow forecasting is improved with the new Spatiotemporal Interactive Learning and Temporal Attention (STIL-TA) model. STIL-TA enhances predictions by dynamically modeling spatial and temporal traffic patterns for better urban congestion management.
Area of Science:
- Transportation Science
- Artificial Intelligence
- Data Science
Background:
- Urban road congestion necessitates accurate traffic flow forecasting.
- Existing models struggle with dynamic spatial dependencies, decoupled spatiotemporal learning, and long-term trend awareness.
Purpose of the Study:
- To propose a novel Spatiotemporal Interactive Learning and Temporal Attention (STIL-TA) model for enhanced traffic flow forecasting.
- To address limitations in existing methods by jointly modeling spatiotemporal characteristics.
Main Methods:
- Developed an interactive learning module using dynamic graph convolution for synchronized spatiotemporal feature interaction.
- Implemented a temporal multi-head trend-aware self-attention mechanism to capture dynamic temporal dependencies and local contextual cues.
Main Results:
- The STIL-TA model demonstrated superior performance compared to existing approaches.
- Significant improvements in forecasting accuracy were observed across four real-world traffic datasets.
Conclusions:
- STIL-TA effectively enhances traffic flow prediction accuracy by integrating dynamic spatiotemporal modeling.
- The proposed model offers a promising solution for alleviating urban road congestion through improved forecasting.
