Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171692
Title: A hybrid deep learning approach for real-time estimation of passenger traffic flow in urban railway systems
Authors: Fu, Xianlei
Wu, Maozhi
Ponnarasu, Sasthikapreeya
Zhang, Limao
Keywords: Engineering::Civil engineering
Issue Date: 2023
Source: Fu, X., Wu, M., Ponnarasu, S. & Zhang, L. (2023). A hybrid deep learning approach for real-time estimation of passenger traffic flow in urban railway systems. Buildings, 13(6), 1514-. https://dx.doi.org/10.3390/buildings13061514
Journal: Buildings 
Abstract: 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 convolutional memory network (GCMN) was constructed and trained for accurate real-time prediction of passenger traffic flow for the MRS. Data collected of the traffic flow in Delhi’s metro rail network system in the period from October 2012 to May 2017 were utilized to demonstrate the effectiveness of the developed model. The results indicate that (1) the developed method provides accurate predictions of the traffic flow with an average coefficient of determination (R2) of 0.920, RMSE of 368.364, and MAE of 549.527, and (2) the GCMN model outperforms state-of-the-art methods, including LSTM and the light gradient boosting machine (LightGBM). This study contributes to the state of practice in proposing a novel framework that provides reliable estimations of passenger traffic flow. The developed model can also be used as a benchmark for planning and upgrading works of the MRS by metro owners and architects.
URI: https://hdl.handle.net/10356/171692
ISSN: 2075-5309
DOI: 10.3390/buildings13061514
Schools: School of Civil and Environmental Engineering 
Rights: © 2023 The authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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