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: Xianlei Fu, Maozhi Wu, Sasthikapreeya Ponnarasu, Limao Zhang
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Buildings
Subjects:
traffic volume prediction
deep learning
real-time prediction
metro railway systems
passenger flow prediction
urban railway system
Online Access:https://www.mdpi.com/2075-5309/13/6/1514

Internet

https://www.mdpi.com/2075-5309/13/6/1514

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