Traffic forecasting with graph spatial-temporal position recurrent network
With the development of social economy and smart technology, the explosive growth of vehicles has caused traffic forecasting to become a daunting challenge, especially for smart cities. Recent methods exploit graph spatial-temporal characteristics, including constructing the shared patterns of traff...
Saved in:
Main Authors: | Chen, Yibi, Li, Kenli, Yeo, Chai Kiat, Li, Keqin |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/172787 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Graph neural network for traffic forecasting: the research progress
by: Jiang, Weiwei, et al.
Published: (2023) -
Attention based graph Bi-LSTM networks for traffic forecasting
by: Zhao, Han, et al.
Published: (2021) -
Approximate personalized propagation for unsupervised embedding in heterogeneous graphs
by: Chen, Yibi, et al.
Published: (2022) -
Determining the Future Demand: Studies for Air Traffic Forecasting Remove
by: Phyoe, Su Myat, et al.
Published: (2016) -
An optimization approach towards air traffic forecasting : a case study of air traffic in Changi airport
by: Sailauov, Tolebi, et al.
Published: (2020)