This paper proposes an improved general regression neural network to predict passenger flow under station closures. First, a random forest classification model is developed to determine the key factors of the reach of a closed station. Then, a generalized regression neural network model is constructed to predict the spatial variation in passenger flow caused by station closures. Furthermore, an improved genetic algorithm is developed to search for optimal model parameters to improve the generalization ability and prediction accuracy of the generalized regression neural network model. A real case study is conducted to verify the proposed approach. Compared with other models, the proposed approach achieves higher prediction accuracy in passenger flow under station closures.
Subway Passenger Flow Forecasting Under Station Closure with an Improved General Regression Neural Network
Lect. Notes Electrical Eng.
International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021
Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021 ; Kapitel : 44 ; 397-405
2022-02-19
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2022
|Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media
Online Contents | 2016
|Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media
Online Contents | 2017
|