Short-time Passenger Flow Forecast of Urban Rail Transit Based on the CEEMDAN-BLSTM Model
Author:
Affiliation:
1. Hohai university,College of energy and electrical engineering,Nanjing,China
2. NARI technology Co.,Ltd,Nanjing,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9712847/9712848/09713560.pdf?arnumber=9713560
Reference21 articles.
1. Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM
2. Cluster-Based LSTM Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit
3. Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction
4. Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction
5. Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Short-term forecasting airport passenger flow during periods of volatility: Comparative investigation of time series vs. neural network models;Journal of Air Transport Management;2024-03
2. Prediction of Short-term Passenger Flow of Urban Rail Transit based on Data Decomposition;2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA);2022-06-24
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