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A hybrid neural network for urban rail transit short-term flow prediction

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Abstract

Accurate and rapid short-term passenger flow prediction is the foundation for safe and efficient operation of urban rail transit systems. The urban rail transit passenger flow is related to the surrounding land properties and is accompanied by random interference. However, the passenger flow characteristics of stations and the role played by random disturbances in predicting passenger flow signals are not clear. A hybrid neural network named Urban Rail Transit Short-Term Flow Prediction Neural Network (URTSTFPNN) is proposed to improve the accuracy and efficiency of short-term passenger flow prediction. The network consists of three modules: feature Processing Module, Data Reconstruction Module, and Prediction Module. In this process, the urban rail transit stations are classified by dynamic time warping based on the time series attributes of entry and exit passenger flow. The noise reduction technology of wavelet transform is added to the network to increase the accuracy of the model. The analysis results of the proposed model using data from Metro Line 2 in Xi’an, Shaanxi Province, China, indicate that urban rail transit stations can be divided into commercial and official stations, high-density residential stations, low-density residential stations, and tourist and passenger transport terminal stations. The URTSTFPNN shows higher predictive accuracy in revealing the errors compared to the single Long Short-Term Memory model, the Auto-Regression and Moving Average model, and the BP neural network model. The coefficient of determination increased by 1.91 ~ 3.48%, 6.45 ~ 9.45%, and 2.72 ~ 5.69%, with a reduction of calculation time by 19.57 ~ 33.29%, 0.88 ~ 11.61%, and 27.87 ~ 36.71%, respectively. The model proposed by this research can accurately and quickly predict passenger flow, which can be used to guide various categories of urban rail stations to develop effective passenger flow management measures.

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Funding

This research is supported by the National Natural Science Foundation of China [grant number 51408049]; the Natural Science Basic Research Program of Shaanxi [grant number 2020JM-237]; the Natural Science Foundation of Henan Province [grant number 242300420029]; and the Fundamental Research Funds for the Central Universities, CHD [grant number 300102342725].

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Authors and Affiliations

  1. School of College of Mechanical and Electrical Engineering, Henan Agricultural University, No. 63 Nongye Road, Zhengzhou, 450002, Henan Province, China

    Caihua Zhu & Zhenfeng Wang

  2. School of College of Vehicle and Traffic Engineering, Zhengzhou University of Science and Technology, No.1 Xueyuan Road, Zhengzhou, 450064, Henan Province, China

    Xiaoli Sun

  3. School of College of Transportation Engineering, Chang’an University, Middle Section of South 2nd Ring Road, Xi’an, 710064, Shaanxi Province, China

    Yuran Li & Yan Li

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  1. Caihua Zhu
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Contributions

Caihua Zhu contributed to data curation, methodology, and writing—original draft preparation. Xiaoli Sun contributed to conceptualization, data curation, software, and validation. Yuran Li, Zhenfeng Wang, and Yan Li contributed to writing—reviewing and editing. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Yan Li.

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Zhu, C., Sun, X., Li, Y. et al. A hybrid neural network for urban rail transit short-term flow prediction. J Supercomput 80, 24297–24323 (2024). https://doi.org/10.1007/s11227-024-06331-2

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