Cargando…
Convolutional Long-Short Term Memory Network with Multi-Head Attention Mechanism for Traffic Flow Prediction
Accurate predictive modeling of traffic flow is critically important as it allows transportation users to make wise decisions to circumvent traffic congestion regions. The advanced development of sensing technology makes big data more affordable and accessible, meaning that data-driven methods have...
Autores principales: | Wei, Yupeng, Liu, Hongrui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607106/ https://www.ncbi.nlm.nih.gov/pubmed/36298345 http://dx.doi.org/10.3390/s22207994 |
Ejemplares similares
-
Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
por: Oluwasanmi, Ariyo, et al.
Publicado: (2023) -
Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction
por: Yu, Kun, et al.
Publicado: (2021) -
Recognition Method of Massage Techniques Based on Attention Mechanism and Convolutional Long Short-Term Memory Neural Network
por: Zhu, Shengding, et al.
Publicado: (2022) -
Multi-Head Attention-Based Long Short-Term Memory for Depression Detection From Speech
por: Zhao, Yan, et al.
Publicado: (2021) -
Prediction of solar irradiance using convolutional neural network and attention mechanism-based long short-term memory network based on similar day analysis and an attention mechanism
por: Hou, Xinxing, et al.
Publicado: (2023)