Pretraining-improved Spatiotemporal graph network for the generalization performance enhancement of traffic forecasting

Pretraining-improved Spatiotemporal graph network for the generalization performance enhancement of traffic forecasting

Abstract Traffic forecasting is considered a cornerstone of smart city development. A key challenge is capturing the long-term spatiotemporal dependencies of traffic data while improving the model’s generalization ability. To address these issues, various sophisticated modules are embedded into diff...

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Bibliographic Details
Main Authors: Xiangyue Zhang, Chao Li, Ling Ji, Yuyun Kang, Mingming Pan, Zhuo Liu, Qiang Qi
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Traffic forecasting
Pre-training
Spatio-temporal graph network
DDPM
State of space model
Online Access:https://doi.org/10.1038/s41598-025-11375-2
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https://doi.org/10.1038/s41598-025-11375-2

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