{"id":2096,"date":"2026-04-02T18:37:33","date_gmt":"2026-04-02T10:37:33","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=2096"},"modified":"2026-05-11T22:20:49","modified_gmt":"2026-05-11T14:20:49","slug":"spatio-temporal-heterogeneous-learning-for-traffic-flow-forecasting-in-smart-transportation","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=spatio-temporal-heterogeneous-learning-for-traffic-flow-forecasting-in-smart-transportation","title":{"rendered":"Spatio-Temporal Heterogeneous learning for Traffic Flow Forecasting in Smart Transportation"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 29, Issue 6<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Kaibin Wei1<\/sup><\/i><\/a>, Jianqiang Jing1<\/sup>, Haifeng Li2<\/sup><\/i><\/a>, Xiannian Xie1<\/sup>, and Furong Li1<\/sup><\/p>\n\n\n\n

1<\/sup>School of Electronic Information and Electrical Engineering, Tianshui Normal University, Tianshui, 741000, China<\/p>\n\n\n\n

2<\/sup>School of Mathematics and Statistics, Fuyang Normal University, Fuyang 236037, China<\/p>\n<\/div>\n\n\n\n

\n

Received: August 23, 2025
Accepted: September 27, 2025
Publication Date: April 2, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

The illustration of STHL, consisting of dual spatio-temporal feature extraction, cluster-invariant spatial heterogeneity learning, and information-driven temporal heterogeneity learning<\/p>\n<\/div>\n<\/div>\n\n\n\n

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

Download Citation:\u00a0 BibTeX <\/a>| https:\/\/doi.org\/10.6180\/jase.202606_29(6).0011<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

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 spatio temporal 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 MAPEmetrics, showcasing its effectiveness in capturing spatio-temporal heterogeneity for enhanced traffic flowprediction accuracy.<\/p>\n\n\n\n

Keywords: Traffic flow forecasting; Spatio-temporal heterogeneous learning; graph contrastive learning<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
    \n
  1. [1] W. Zhao, G. Yuan, Y. Zhang, X. Liu, S. Liu, and L. Zhang, (2025) \u201cAn Interpretable and Efficient Multi-scale Spatio-Temporal Neural Network for Traffic Flow Forecasting\u201d Expert Systems with Applications: 128961. DOI: 10.1016\/j.eswa.2025.128961.<\/li>\n
  2. [2] Y. Luo, J. Zheng, X. Wang, X. Jiang, Z. Zhu, et al., (2025) \u201cST-GMLP: A concise spatial-temporal framework based on gated multi-layer perceptron for traffic flow forecasting\u201d Neural Networks 184: 107074. DOI: 10.1016\/j.neunet.2024.107074.<\/li>\n
  3. [3] J. Gao, M. Liu, P. Li, A. A. Laghari, A. R. Javed, N. Victor, and T. R. Gadekallu, (2023) \u201cDeep Incomplete Multiview Clustering via Information Bottleneck for Pattern Mining of Data in Extreme-Environment IoT\u201d IEEE Internet of Things Journal 11(16): 26700\u201326712. DOI: 10.1109\/JIOT.2023.3325272.<\/li>\n
  4. [4] W. Hu, Y. Wu, and Z. Yang, (2024) \u201cAn analysis of credit risk prediction for small and micro enterprises\u201d Journal of Artificial Intelligence Research 1(2): 1\u201314. DOI: 10.70891\/JAIR.2024.110004.<\/li>\n
  5. [5] J. Gao, M. Liu, P. Li, J. Zhang, and Z. Chen, (2024) \u201cDeep Multiview Adaptive Clustering With Semantic Invariance\u201d IEEE Transactions on Neural Networks and Learning Systems 35(9): 12965\u201312978. DOI: 10.1109\/TNNLS.2023.3265699.<\/li>\n
  6. [6] S. Mandia, R. Mitharwal, and K. Singh, (2024) \u201cAutomatic student engagement measurement using machine learning techniques: A literature study of data and methods\u201d Multimedia Tools and Applications 83(16): 49641\u201349672. DOI: doi.org\/10.1007\/s11042-023-17534-9.<\/li>\n
  7. [7] Q. Luo, S. He, X. Han, Y. Wang, and H. Li, (2024) \u201cLSTTN: A long-short term transformer-based spatiotemporal neural network for traffic flow forecasting\u201d Knowledge-Based Systems 293: 111637. DOI: 10.1016\/j.knosys.2024.111637.<\/li>\n
  8. [8] W. Kong, Z. Guo, and Y. Liu. \u201cSpatio-temporal pivotal graph neural networks for traffic flow forecasting\u201d. In: Proceedings of the AAAI conference on artificial intelligence. 38. 8. 2024, 8627\u20138635. DOI: 10.1609\/aaai.v38i8.28707.<\/li>\n
  9. [9] Z. Li, J. Zhou, Z. Lin, and T. Zhou, (2024) \u201cDynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting\u201d Knowledge-based systems 297: 111946. DOI: 10.1016\/j.knosys.2024.111946.<\/li>\n
  10. [10] Q. Bing, P. Zhao, C. Ren, X. Wang, and Y. Zhao, (2024) \u201cShort-term traffic flow forecasting method based on secondary decomposition and conventional neural network-transformer\u201d Sustainability 16(11): 4567. DOI: 10.3390\/su16114567.<\/li>\n
  11. [11] Z. Fang, Q. Long, G. Song, and K. Xie. \u201cSpatial-temporal graph ode networks for traffic flow forecasting\u201d. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2021, 364\u2013373.<\/li>\n
  12. [12] G. Huo, Y. Zhang, B. Wang, J. Gao, Y. Hu, and B. Yin, (2023) \u201cHierarchical spatio-temporal graph convolutional networks and transformer network for traffic flow forecasting\u201d IEEE Transactions on Intelligent Transportation Systems 24(4): 3855\u20133867. DOI: 10.1109\/TITS.2023.3234512.<\/li>\n
  13. [13] S. Lan, Y. Ma, W. Huang, W. Wang, H. Yang, and P. Li. \u201cDstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting\u201d. In: International conference on machine learning. 2022, 11906\u201311917.<\/li>\n
  14. [14] C. Chen, Y. Liu, L. Chen, and C. Zhang, (2022) \u201cBidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting\u201d IEEE Transactions on Neural Networks and Learning Systems 34(10): 6913\u20136925. DOI: 10.1109\/TNNLS.2022.3183903.<\/li>\n
  15. [15] Y. Zhao, X. Luo, W. Ju, C. Chen, X.-S. Hua, and M. Zhang. \u201cDynamic hypergraph structure learning for traffic flow forecasting\u201d. In: 2023 IEEE 39th International Conference on Data Engineering (ICDE). 2023, 2303\u20132316.<\/li>\n
  16. [16] J. Gao, C. Guo, Y. Liu, P. Li, J. Zhang, and M. Liu. \u201cDynamic-static Feature Fusion with Multi-scale Attention for Continuous Blood Glucose Prediction\u201d. In: IEEE International Conference on Acoustics, Speech and Signal Processing. 2025, 1\u20135. DOI: 10.1109\/ICASSP49660.2025.10888380.<\/li>\n
  17. [17] J. Ji, J. Wang, C. Huang, J. Wu, B. Xu, Z. Wu, J. Zhang, and Y. Zheng. \u201cSpatio-temporal self-supervised learning for traffic flow prediction\u201d. In: Proceedings of the AAAI conference on artificial intelligence. 37. 4. 2023, 4356\u20134364. DOI: 10.1609\/aaai.v37i4.25555.<\/li>\n
  18. [18] Y. Jiang and S. Yin, (2023) \u201cHeterogenous-view occluded expression data recognition based on cycle-consistent adversarial network and K-SVD dictionary learning under intelligent cooperative robot environment\u201d Computer Science and Information S<\/span>ystems 20(4): 1869\u20131883. DOI: 10.2298\/CSIS221228034J.<\/li>\n
  19. [19] Y. Qi, Z. Men, and S. Xie, (2025) \u201cResearch on Wind and Photovoltaic Power Generation Forecasting Method Based on Digital Twin and Deep Learning\u201d Software Engineering 28(03): 57\u201363. DOI: 10.19644 \/ j.cnki.issn2096-1472.2025.003.011.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,15,6,121],"tags":[249],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" 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:\u00a0 BibTeX | https:\/\/doi.org\/10.6180\/jase.202606_29(6).0011\u00a0\u00a0 Download PDF Accurate traffic flow forecasting is crucial for enhancing urban transportation efficiency…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/2096"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2096"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2096"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}