ST_AGCNT: Traffic Speed Forecasting Based on Spatial–Temporal Adaptive Graph Convolutional Network with Transformer

Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are...

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Veröffentlicht in:Sustainability Jg. 17; H. 5; S. 1829
Hauptverfasser: Cheng, Rongjun, Liu, Mengxia, Xu, Yuanzi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.03.2025
Schlagworte:
Convolutional codes
Deep learning
Forecasts and trends
Graph theory
Neural networks
Speed
Speed limits
Statistical methods
Time series
Traffic flow
China
ISSN:2071-1050, 2071-1050
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Zusammenfassung:Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, effectively modeling long-range temporal features is crucial for prediction accuracy. In order to overcome these challenges, a Spatial–Temporal Adaptive Graph Convolutional Network with Transformer (ST_AGCNT) is designed in this paper. Specifically, an adaptive graph convolution network (AGCN) is designed to extract spatial dependency. An adaptive graph that fuses predefined matrices and learnable matrix is proposed to learn the correlations between nodes. The predefined matrices provide the model with richer prior information, while the learnable matrix can extract the dynamic nature of the nodes. And a temporal transformer (TT) is proposed to extract the long-range temporal dependency. In addition, to learn more information to achieve better results, different historical segments are modeled. Experiments conducted on a real-world traffic dataset confirm the effectiveness of the proposed model when compared to other baseline models. This model demonstrated excellent performance in prediction tasks across different time steps, effectively accomplishing traffic speed forecasting. It provides data support for improving traffic efficiency and reducing resource waste, contributing to the sustainable development of traffic management.
Bibliographie:ObjectType-Article-1
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ISSN:2071-1050
2071-1050
DOI:10.3390/su17051829