Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)
Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some metho...
Saved in:
Main Authors: | Zhang, Ying, Xu, Shimin, Zhang, Linghui, Jiang, Weiwei, Alam, Sameer, Xue, Dabin |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180117 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Attention based graph Bi-LSTM networks for traffic forecasting
by: Zhao, Han, et al.
Published: (2021) -
Sentinel-1 spatiotemporal simulation using convolutional LSTM for flood mapping
by: Ulloa, Noel Ivan, et al.
Published: (2022) -
Hashtag recommendation with topical attention-based LSTM
by: LI, Yang, et al.
Published: (2016) -
A CNN-LSTM-attention model for near-crash event identification on mountainous roads
by: Zhao, Jing, et al.
Published: (2024) -
A convolutional stacked bidirectional LSTM with a multiplicative attention mechanism for aspect category and sentiment detection
by: Kumar, Ashok J., et al.
Published: (2022)