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September 10, 2025

Structure‐Enhanced Graph Learning Approach for Traffic Flow and Density Forecasting

Key Points

The SGL4TF model significantly enhances traffic forecasting accuracy by integrating graph convolutional networks.
Extensive experiments on real-world datasets show that the SGL4TF model outperforms existing state-of-the-art traffic forecasting techniques.
Using a seq2seq framework, the model effectively captures long-term temporal dependencies in traffic data.
The approach addresses challenges in preserving structural and topological features, leading to better prediction outcomes.

Abstract

ABSTRACT The rapid expansion of Internet infrastructure and artificial intelligence (AI) has significantly advanced intelligent transportation systems (ITS), which are considered as essential for automating traffic monitoring and management in smart cities. Among ITS applications, traffic flow and density prediction are considered as important problem for optimizing transportation planning and reducing congestion. In recent years, deep learning models, particularly recurrent neural networks (RNNs) and graph neural networks (GNNs), have been widely utilized for traffic forecasting. These models can support to effectively capture temporal and spatial dependencies in traffic data, as a result enabling more accurate forecasting. Despite advancements, recently proposed RNN‐GNN‐based forecasting models still face challenges related to the capability of preserving rich structural and topological features from traffic networks. The complex spatial dependencies inherent in road connections and vehicle movement patterns are often underrepresented; therefore, limiting the forecasting accuracy. To address these limitations, in this paper, we propose SGL4TF, a structure‐enhanced graph learning model that integrates graph convolutional networks (GCN) with a sequence‐to‐sequence (seq2seq) framework. This architecture enhances the ability to jointly model spatial relationships and long‐term temporal dependencies, hence can lead to more precise traffic predictions. Our approach introduces a deeper graph‐structural learning mechanism using nonlinear transformations within GNN layers, which can effectively assist to improve structural feature extraction while mitigating over‐smoothing issues. The seq2seq component further refines temporal correlations, enabling long‐term traffic state predictions. Extensive experiments on real‐world datasets demonstrate our proposed SGL4TF model's superior performance over state‐of‐the‐art traffic forecasting techniques.

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