Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory

Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory

Traffic prediction is a vitally important keystone of an intelligent transportation system (ITS). It aims to improve travel route selection, reduce overall carbon emissions, mitigate congestion, and enhance safety. However, efficiently modelling traffic flow is challenging due to its dynamic and non...

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Bibliographic Details
Main Authors: Selim Reza, Marta Campos Ferreira, José J. M. Machado, João Manuel R. S. Tavares
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Applied Sciences
Subjects:
support vector regression
long short-term memory
gated recurrent units
one-dimensional convolution neural network
Zenodo and PeMS datasets
Online Access:https://www.mdpi.com/2076-3417/12/10/5149

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https://www.mdpi.com/2076-3417/12/10/5149

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