SDSCNN: A Hybrid Model Integrating Static and Dynamic Spatial Correlation Neural Network for Traffic Prediction

SDSCNN: A Hybrid Model Integrating Static and Dynamic Spatial Correlation Neural Network for Traffic Prediction

Traffic flow prediction is of great significance for traffic control, and it has been challenging for capturing the complex spatial-temporal correlation. However, most existing prediction methods only consider the spatial adjacency of the nodes (i.e., static spatial correlation), lacking sufficient...

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Bibliographic Details
Main Authors: Junming Dai, Jiashuang Huang, Qinqin Shen, Quan Shi, Siyun Feng, Zhenquan Shi
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Traffic flow prediction
static and dynamic spatial correlation
graph attention network
multi-head self-attention mechanism
Online Access:https://ieeexplore.ieee.org/document/9953093/

Internet

https://ieeexplore.ieee.org/document/9953093/

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