Short-Term Passenger Flow Prediction for Urban Rail Stations Using Learning Network Based on Optimal Passenger Flow Information Input Algorithm

Short-Term Passenger Flow Prediction for Urban Rail Stations Using Learning Network Based on Optimal Passenger Flow Information Input Algorithm

Massive short-term passenger flow prediction models of urban rail transit stations have been used in different conditions. However, researchers encountered several challenges while selecting the optimal passenger flow information input matrix and eliminating the redundant information in the original...

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Bibliographic Details
Main Authors: Bo Wang, Mao Ye, Zhenjun Zhu, Yan Li, Qiangsheng Liang, Jian Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Urban rail transit
short-term passenger flow prediction
optimal passenger flow information input algorithm (OPIIA)
long and short-time memory network (LSTM)
multi-factors
Online Access:https://ieeexplore.ieee.org/document/9197667/

Internet

https://ieeexplore.ieee.org/document/9197667/

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