STFGCN: Spatio-Temporal Fusion Graph Convolutional Networks for Subway Traffic Prediction
Metro passenger flow prediction is a crucial and challenging task in the intelligent transportation system of subways. It serves as the foundation for achieving intelligent transportation in subway systems and holds significant importance in practical applications. Although much progress has been ma...
Saved in:
| Main Authors: | Xiaoxi Zhang, Zhanwei Tian, Yan Shi, Qingwen Guan, Yan Lu, Yujie Pan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10807236/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Frequency Enhanced Dynamic Graph Convolutional Networks for Traffic Flow Forecasting
by: Liyuan Wang, et al.
Published: (2025-01-01) -
Spatio-temporal Graph Convolutional Neural Network for traffic signal prediction in large-scale urban networks
by: Shimon Komarovsky, et al.
Published: (2025-07-01) -
Multi dynamic temporal representation graph convolutional network for traffic flow prediction
by: Zuojun Wu, et al.
Published: (2025-05-01) -
STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast
by: Jiahao Chang, et al.
Published: (2025-05-01) -
Grid Partition-Based Dynamic Spatial–Temporal Graph Convolutional Network for Large-Scale Traffic Flow Forecasting
by: Lifeng Gao, et al.
Published: (2025-05-01)