sAMDGCN: sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network for Traffic Flow Forecasting

sAMDGCN: sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network for Traffic Flow Forecasting

Accurate traffic flow prediction plays a vital role in intelligent transportation systems, helping traffic management departments maintain stable traffic order, reduce traffic congestion, and improve road safety. Existing prediction methods focus on dynamic modeling of the spatiotemporal dependencie...

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Bibliographic Details
Main Authors: Shiyuan Zhang, Yanni Ju, Weishan Kong, Hong Qu, Liwei Huang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Mathematics
Subjects:
traffic flow prediction
spatiotemporal dependency
sLSTM
attention
graph convolutional network
Online Access:https://www.mdpi.com/2227-7390/13/2/185

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https://www.mdpi.com/2227-7390/13/2/185

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