Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM

Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM

Passenger flow prediction is important for the operation of urban rail transit. The prediction of abnormal passenger flow is difficult due to rare similar history data. A model based on the fusion of support vector regression (SVR) and long short-term memory (LSTM) neural network is proposed. The in...

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Bibliographic Details
Main Authors: Jianyuan Guo, Zhen Xie, Yong Qin, Limin Jia, Yaguan Wang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Short-term passenger flow prediction
urban rail transit
support vector regression (SVR)
long short-term memory (LSTM)
Online Access:https://ieeexplore.ieee.org/document/8675737/

Internet

https://ieeexplore.ieee.org/document/8675737/

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