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Spatiotemporal-decoupled interactive learning for traffic flow prediction.

Linlong Chen1, Qingfang Wu2

  • 1School of Big Data & Information Engineering, Guiyang Institute of Humanities and Technology, Guiyang, 550025, China. chenlinlong1009@yeah.net.

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|February 14, 2026
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Summary

This study introduces Spatiotemporal-Decoupled Interactive Learning (STDIL) for improved traffic flow prediction. STDIL enhances accuracy by better capturing complex spatiotemporal patterns in diverse urban traffic scenarios.

Keywords:
Graph neural networksInteractive learningSpatiotemporal decouplingSpatiotemporal heterogeneityTraffic flow prediction

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Area of Science:

  • Intelligent Transportation Systems
  • Data Science
  • Network Science

Background:

  • Accurate traffic flow prediction is crucial for intelligent transportation systems (ITS).
  • Existing methods often fail to capture complex spatiotemporal dependencies and diverse patterns due to spatial heterogeneity and temporal variations.
  • This limits the effectiveness of trip planning, network dispatch, and management decisions.

Purpose of the Study:

  • To propose a novel framework, Spatiotemporal-Decoupled Interactive Learning (STDIL), to address the limitations of existing traffic flow prediction methods.
  • To enhance the learning of spatiotemporal dependencies and accommodate pattern diversity in traffic flow data.
  • To improve the accuracy and adaptability of traffic flow prediction models.

Main Methods:

  • The proposed STDIL framework integrates a spatiotemporal decoupling module and an interactive learning module.
  • The spatiotemporal decoupling module reconstructs sequences along spatial and temporal dimensions for discriminative representations.
  • The interactive learning module dynamically reconstructs graph structures to capture global and local spatiotemporal correlations, including long-range dependencies.

Main Results:

  • Experiments on four real-world urban traffic flow datasets demonstrated that STDIL significantly outperforms existing methods across all prediction horizons.
  • STDIL effectively handles spatiotemporal heterogeneity and dynamic dependencies inherent in traffic data.
  • The framework shows adaptability to diverse traffic scenarios, achieving higher prediction accuracy.

Conclusions:

  • STDIL provides a more effective approach to traffic flow prediction by addressing limitations in existing methods.
  • The framework's ability to capture complex spatiotemporal interactions leads to significant accuracy improvements.
  • STDIL offers a promising solution for enhancing the capabilities of intelligent transportation systems.