A Novel Attention-Based Dynamic Multi-Graph Spatial-Temporal Graph Neural Network Model for Traffic Prediction

Traffic flow prediction is a non-negligible part of intelligent transportation and mobility. Unfortunately, the unique non-linearity and complex spatial-ST-correlation of transport flow data suggest considerable challenges in prediction. The dynamic interaction of multiple spatial relations greatly...

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Vydáno v:IEEE transactions on emerging topics in computational intelligence Ročník 9; číslo 2; s. 1910 - 1923
Hlavní autoři: Diao, Chunyan, Zhang, Dafang, Liang, Wei, Jiang, Man, Li, Kuanching
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithm design and analysis
Algorithms
attention mechanism
Attention mechanisms
Computational modeling
Convolution
Correlation
Data models
Feature extraction
Graph conventional network
Graph convolutional networks
Graph neural networks
multi-graph
Prediction models
Predictive models
Road traffic
Semantics
spatial-temporal system
Spatiotemporal phenomena
Traffic flow
traffic prediction
ISSN:2471-285X, 2471-285X
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Shrnutí:Traffic flow prediction is a non-negligible part of intelligent transportation and mobility. Unfortunately, the unique non-linearity and complex spatial-ST-correlation of transport flow data suggest considerable challenges in prediction. The dynamic interaction of multiple spatial relations greatly influences traffic flow prediction. However, the existing spatial-temporal prediction algorithms are based on graph convolution to capture global or heterogeneous relationships, and simpler graph convolution models cannot accurately capture complex dynamic spatial relationships. To address the issues as mentioned above, this study proposes an attention-based multi-graph dynamic spatial-temporal prediction model ADMSTGCN to capture a variety of dynamic interaction relationships in traffic flow. First, we use a distance graph to explore the relationships between adjacent distances and use a semantic graph to mine spatial relationships between nodes that are far apart but have similar relationships, then fuse these two graphs to obtain a fusion graph with multiple spatial interaction relationships. The correlations between different neighbors are then further learned through a dynamic multi-graph spatial-temporal learning module that aggregates the features of different neighbors through gated graph convolution and attention mechanisms to capture various dynamic and complex spatial-temporal interactions. Experimental evaluations show that the framework proposed outperforms existing methods with better results in the analysis performed with publicly available datasets and also demonstrates the importance of capturing multiple interactions of spatial-temporal relationships.
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ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2024.3462513