Cargando…
GLSNN Network: A Multi-Scale Spatiotemporal Prediction Model for Urban Traffic Flow
Traffic flow prediction is a key issue in intelligent transportation systems. The growing trend in data disclosure has created more potential sources for the input for predictive models, posing new challenges to the prediction of traffic flow in the era of big data. In this study, the prediction of...
Autores principales: | Cai, Benhe, Wang, Yanhui, Huang, Chong, Liu, Jiahao, Teng, Wenxin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694770/ https://www.ncbi.nlm.nih.gov/pubmed/36433477 http://dx.doi.org/10.3390/s22228880 |
Ejemplares similares
-
GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity
por: Zhao, Dongcheng, et al.
Publicado: (2020) -
Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction
por: Oluwasanmi, Ariyo, et al.
Publicado: (2023) -
Road traffic flow prediction based on dynamic spatiotemporal graph attention network
por: Chen, Yuguang, et al.
Publicado: (2023) -
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
por: Yu, Haiyang, et al.
Publicado: (2017) -
MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction
por: Huang, Xiaohui, et al.
Publicado: (2023)