A hybrid deep learning approach for real-time estimation of passenger traffic flow in urban railway systems

A hybrid deep learning approach for real-time estimation of passenger traffic flow in urban railway systems

This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph convol...

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Bibliographic Details
Main Authors: Fu, Xianlei, Wu, Maozhi, Ponnarasu, Sasthikapreeya, Zhang, Limao
Other Authors: School of Civil and Environmental Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Civil engineering
Traffic Volume Prediction
Deep Learning
Online Access:https://hdl.handle.net/10356/171692

Internet

https://hdl.handle.net/10356/171692

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