A hybrid neural network for urban rail transit short-term flow prediction-Reference-Cited by-同舟云学术

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

Published:2024-07-03 Issue:16 Volume:80 Page:24297-24323
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Zhu Caihua,Sun Xiaoli,Li Yuran,Wang Zhenfeng,Li Yan

Funder

the Fundamental Research Funds for the Central Universities, CHD

the National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06331-2.pdf

Reference61 articles.

1. Lu WB, Zhang Y, Li PK, Wang T (2023) Mul-DesLSTM: an integrative multi-time granularity deep learning prediction method for urban rail transit short-term passenger flow. Eng Appl Artif Intell 125:106741. https://doi.org/10.1016/j.engappai.2023.106741

2. Xue QC, Zhang W, Ding ML, Yang X, Wu JJ, Gao ZY (2023) Passenger flow forecasting approaches for urban rail transit: a survey. Int J Gen Syst 52(8):919–947. https://doi.org/10.1080/03081079.2023.2231133

3. Yu Q, Zhang YD, Guo J, Lai P, Ma L (2023) Short-term inbound passenger flow forecasting for urban rail transit based on deep ensemble neural network. J China Railway Soc 45(12):37–46. https://doi.org/10.3969/j.issn.1001-8360.2023.12.004

4. Yang F, Zhang HY, Tao SM (2021) Travel order quantity prediction via attention-based bidirectional LSTM networks. J Supercomput 78(3):4398–4420. https://doi.org/10.1007/s11227-021-04032-8

5. Yang X, Xue QC, Ding ML, Wu JJ, Gao ZY (2021) Short-term prediction of passenger volume for urban rail systems: a deep learning approach based on smart-card data. Int J Prod Econ 231:107920. https://doi.org/10.1016/j.ijpe.2020.107920

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