Article: Transformer-GNN hybrid architecture for optimising real-time traffic forecasting on highways Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.9 pp.38 - 50 Abstract: Facing the challenge of worsening highway traffic congestion, precise real-time forecasting is crucial for intelligent traffic management. However, traditional models struggle to effectively capture the complex spatio-temporal dependencies and dynamic propagation delays inherent in traffic data. To address this, this paper proposes a hybrid architecture that integrates graph neural networks with transformers. Through a dynamic graph attention mechanism and a delay-aware module, it significantly enhances the modelling capabilities for long-range spatial correlations and temporal propagation effects. Experiments on public datasets such as performance measurement system 04 and performance measurement system 08 demonstrate that the proposed model reduces the mean absolute error by 6.2%-9.2% compared to existing state-of-the-art methods within the 15-60 minute prediction window, with particularly notable performance improvements during peak congestion periods. The framework presented here has the potential to provide a more reliable technical pathway for traffic state prediction, holding significant practical application value. Inderscience Publishers - linking academia, business and industry through research

Title: Transformer-GNN hybrid architecture for optimising real-time traffic forecasting on highways

Authors: Hua Cheng; Yupeng Cao; Weiping Li

Addresses: Ji'andong Management Center, Jiangxi Communications Investment Group Co., LTD., Ji'an, 343700, China ' Ji'andong Management Center, Jiangxi Communications Investment Group Co., LTD., Ji'an, 343700, China ' Shandong Transport Vocational College, Weifang, 261206, China

Abstract: Facing the challenge of worsening highway traffic congestion, precise real-time forecasting is crucial for intelligent traffic management. However, traditional models struggle to effectively capture the complex spatio-temporal dependencies and dynamic propagation delays inherent in traffic data. To address this, this paper proposes a hybrid architecture that integrates graph neural networks with transformers. Through a dynamic graph attention mechanism and a delay-aware module, it significantly enhances the modelling capabilities for long-range spatial correlations and temporal propagation effects. Experiments on public datasets such as performance measurement system 04 and performance measurement system 08 demonstrate that the proposed model reduces the mean absolute error by 6.2%-9.2% compared to existing state-of-the-art methods within the 15-60 minute prediction window, with particularly notable performance improvements during peak congestion periods. The framework presented here has the potential to provide a more reliable technical pathway for traffic state prediction, holding significant practical application value.

Keywords: traffic flow prediction; graph neural networks; GNNs; transformers; intelligent transportation systems.

DOI: 10.1504/IJRIS.2026.152190

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.9, pp.38 - 50

Received: 16 Nov 2025
Accepted: 23 Dec 2025
Published online: 10 Mar 2026 *

Title: Transformer-GNN hybrid architecture for optimising real-time traffic forecasting on highways

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