Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data
Traffic prediction is essential for advanced traffic planning, design, management, and network sustainability. Current prediction methods are mostly offline, which fail to capture the real-time variation of traffic flows. This paper establishes a sustainable online generative adversarial network (GA...
Enregistré dans:
Auteurs principaux: | Tuo Sun, Bo Sun, Zehao Jiang, Ruochen Hao, Jiemin Xie |
---|---|
Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/e47cb2e205de4e02832f6869a1831cc9 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Textured Mesh Generation Using Multi-View and Multi-Source Supervision and Generative Adversarial Networks
par: Mingyun Wen, et autres
Publié: (2021) -
Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network
par: Chuan Du, et autres
Publié: (2021) -
Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures
par: Zan Li, et autres
Publié: (2021) -
Hybrid Deep Spatio-Temporal Models for Traffic Flow Prediction on Holidays and Under Adverse Weather
par: Wensong Zhang, et autres
Publié: (2021) -
Low-Light Image Enhancement Based on Generative Adversarial Network
par: Nandhini Abirami R., et autres
Publié: (2021)