Spatio-Temporal Heterogeneous learning for Traffic Flow Forecasting in Smart Transportation

Kaibin  Wei; Jianqiang  Jing; Haifeng  Li; Xiannian  Xie; Furong  Li

doi:10.6180/jase.202606_29(6).0011

Spatio-Temporal Heterogeneous learning for Traffic Flow Forecasting in Smart Transportation

Electrical Engineering Mathematics / Statistics

Kaibin Wei¹This email address is being protected from spambots. You need JavaScript enabled to view it., Jianqiang Jing¹, Haifeng Li²This email address is being protected from spambots. You need JavaScript enabled to view it., Xiannian Xie¹, and Furong Li¹

¹School of Electronic Information and Electrical Engineering, Tianshui Normal University, Tianshui, 741000, China

²School of Mathematics and Statistics, Fuyang Normal University, Fuyang 236037, China

Received: August 23, 2025
Accepted: September 27, 2025
Publication Date: October 24, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202606_29(6).0011

Accurate traffic flow forecasting is crucial for enhancing urban transportation efficiency and travel experiences. However, existing methods face challenges in capturing the complex spatio-temporal heterogeneity of traffic data. This paper introduces a novel Spatio-Temporal Heterogeneous Learning (STHL) framework for traffic flow forecasting. The framework encompasses three key components: dualspatio-temporal feature extraction, cluster invariant spatial heterogeneity learning, and information-driven temporal heterogeneity learning. Dual spatiotemporal feature extraction employs semantic and structural augmentations to enrich traffic flow representation learning, capturing spatial and temporal dependencies comprehensively. Cluster-invariant spatial heterogeneity learning distinguishes traffic patterns across urban regions, while information-driven temporal heterogeneity learning injects time- aware heterogeneity into node representations. Experiments on four real- world traffic flow datasets demonstrate that our method outperforms existing state-of-the-art approaches in terms of MAE and MAPE metrics, showcasing its effectiveness in capturing spatio-temporal heterogeneity for enhanced traffic f low prediction accuracy.

Keywords: Traffic flow forecasting; Spatio-temporal heterogeneous learning; graph contrastive learning

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