Intrinsic Mode Decomposition and Combined Deep Learning Prediction of Urban Rail Transit Passenger Flow at Variable Time Scales

ZHU Guangyu; SUN Xinni; YANG Rongzheng; LIU Kanglin; WEI Yun; WU Bo

doi:10.11999/JEIT221300

Volume 45 Issue 12

Dec. 2023

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ZHU Guangyu, SUN Xinni, YANG Rongzheng, LIU Kanglin, WEI Yun, WU Bo. Intrinsic Mode Decomposition and Combined Deep Learning Prediction of Urban Rail Transit Passenger Flow at Variable Time Scales[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4421-4430. doi: 10.11999/JEIT221300

Citation:

ZHU Guangyu, SUN Xinni, YANG Rongzheng, LIU Kanglin, WEI Yun, WU Bo. Intrinsic Mode Decomposition and Combined Deep Learning Prediction of Urban Rail Transit Passenger Flow at Variable Time Scales[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4421-4430. doi: 10.11999/JEIT221300

Citation:

ZHU Guangyu, SUN Xinni, YANG Rongzheng, LIU Kanglin, WEI Yun, WU Bo. Intrinsic Mode Decomposition and Combined Deep Learning Prediction of Urban Rail Transit Passenger Flow at Variable Time Scales[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4421-4430. doi: 10.11999/JEIT221300

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Intrinsic Mode Decomposition and Combined Deep Learning Prediction of Urban Rail Transit Passenger Flow at Variable Time Scales

doi: 10.11999/JEIT221300

1.
Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China
2.
Beijing Mass Transit Railway Operation Corporation Limited, Beijing 100014, China
3.
Taiyuan China Railway Rail Transit Construction and Operation Co., Ltd, Taiyuan 030006, China

Funds: The Fundamental Research Funds for the Central Universities(2022JBZX024), The National Natural Science Foundation of China (62272036, 62173167, 62132003)

Received Date: 2022-10-14
Rev Recd Date: 2023-02-22

Available Online: 2023-03-13

Publish Date: 2023-12-26

Abstract

Abstract

Different operational states of urban rail transit usually correspond to different Intrinsic Mode Functions (IMFs) and time-scale characteristics in passenger flow time series. A combined deep learning prediction model for short-term passenger flow time series of subway is proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Bidirectional Long Short Term Memory network (BiLSTM), including: mode decomposition of passenger flow time series based on the CEEMDAN algorithm. The sample entropy and hierarchical clustering are used respectively to analyze the complexity and similarity of IMFs. The IMFs are then classified, merged and reconstructed on this basis. The hyper-parameters of the model are optimized using the Tree-structured Parzen Estimator (TPE) in the Optuna framework, and the combined prediction model CEEMDAN-TPE-BiLSTM is established. Actual data are used to validate the model. The results show that the accuracy and validity indicators of the model all reach the optimum for passenger flow time series data with specific characteristics.

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References(20)

References

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