Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which...

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Bibliographic Details
Main Authors: Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Sensors
Subjects:
transportation network
traffic congestion forecasting
spatial-temporal correlation
deep learning
end-to-end
deep autoencoder
convolutional neural network
long short-term memory
Online Access:https://www.mdpi.com/1424-8220/19/10/2229

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