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Short⁃term passenger flow forecasting of urban rail transit based on recurrent neural network
更新时间：2025-05-26

    • Short⁃term passenger flow forecasting of urban rail transit based on recurrent neural network

    • “In the field of short-term passenger flow prediction in urban rail transit, experts have proposed a prediction method based on recurrent neural network model, and verified that 5 minutes is the optimal prediction time granularity. The GRU model has better overall performance than the LSTM model.”
    • Journal of Jilin University(Engineering and Technology Edition)   Vol. 53, Issue 2, Pages: 430-438(2023)
    • 作者机构：

      1.华侨大学 计算机科学与技术学院， 福建 厦门 361021

      2.北京工业大学 信息学部软件学院， 北京 100124

      3.南威软件股份有限公司， 福建 泉州 362000

    • DOI：10.13229/j.cnki.jdxbgxb20210720    

      CLC： U121

    • Received：30 July 2021，

      Published：01 February 2023

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  • ZHANG Hui-zhen,GAO Zheng-kai,LI Jian-qiang,et al.Short⁃term passenger flow forecasting of urban rail transit based on recurrent neural network[J].Journal of Jilin University(Engineering and Technology Edition),2023,53(02):430-438. DOI： 10.13229/j.cnki.jdxbgxb20210720.

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