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2024 | OriginalPaper | Buchkapitel

DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction

verfasst von : Hui Dong, Xiao Pan, Xiao Chen, Jing Sun, Shuhai Wang

Erschienen in: Spatial Data and Intelligence

Verlag: Springer Nature Singapore

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Abstract

The transformer-based method is a popular choice for medium and long-term traffic prediction. However, it still suffers from some problems. The first is that spatial position embedding has poor interpretability. Additionally, the spatial-temporal correlation learning can struggle to reflect the actual complexity of traffic networks relationships. To address the above problems, we propose a traffic prediction framework for dynamic adaptive spatial-temporal graph transformer (DyAdapTransformer). Our method uses the method of random walk to embed the spatial position. The analyzability between transition probability and spatial position representation enhances the interpretability of the model. When learning spatial-temporal correlation, a method of dynamic adaptive graph attention network is proposed. We compared with our framework with four baselines on three datasets. The results show that DyAdapTransformer has a better predictive performance.

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Vorheriges Kapitel Spatio-Temporal Sequence Prediction of Diversion Tunnel Based on Machine Learning Multivariate Data Fusion

Nächstes Kapitel Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model

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Titel: DyAdapTransformer: Dynamic Adaptive Spatial-Temporal Graph Transformer for Traffic Prediction
verfasst von: Hui Dong
Xiao Pan
Xiao Chen
Jing Sun
Shuhai Wang
Verlag: Springer Nature Singapore
Buch: Spatial Data and Intelligence
Print ISBN: 978-981-9729-65-4

Electronic ISBN: 978-981-9729-66-1

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2966-1_17

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