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Attention dynamic graph convolutional network for traffic flow prediction
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AI
DOI：10.1016/j.engappai.2025.112642.png)
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Abstract
En 中文
Traffic flow prediction is crucial for intelligent transportation systems, enabling effective urban planning, traffic management, and emergency response. Existing methods rely on adjacency matrices to model traffic network connections, often failing to capture real-time dynamics and complex spatiotemporal dependencies due to static or data-limited dynamic matrices. To address these challenges, we propose an Attention Dynamic Graph Convolutional Network (ADGCN) that integrates an adaptive dynamic graph convolutional network with a novel lightweight Gated Recurrent Unit and an enhanced attention mechanism. The lightweight gated recurrent unit offers significant efficiency gains over the standard gated recurrent unit. By optimizing its gating mechanism and shrinking the hidden layer dimension, it features a lower number of parameters and achieves a 19 %–47 % reduction in training time. These improvements make it highly suitable for deployment on resource-constrained devices and for use in real-time traffic applications. The dynamic graph generation method, leveraging input features and node embeddings with normalization and nonlinear transformations, effectively captures evolving spatial dependencies without predefined graph structures, enhancing adaptability to fluctuating traffic conditions. The improved attention mechanism strengthens inter-channel feature dependencies, enabling the model to focus on task-critical features and boosting prediction accuracy. Validated on six real-world datasets, ADGCN outperforms most state-of-the-art models on key metrics. It also demonstrates remarkable efficiency, with up to a 47 % reduction in training time and excellent real-time performance, making it highly suitable for Intelligent Transportation Systems.
Keywords:
Traffic flow prediction
Graph convolutional networks
Attention mechanism
Adaptive dynamic graph
Lightweight gated recurrent unit
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