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Deep learning prediction for subway section passenger flow integrating physical information and snow geese optimization
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Jiahui Wan1, Xiaoxia Yang**, 1, Yuanlei Kang2, Chuang Shao3
China Safety Science Journal | 2026, 36(4) : 244 - 251
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China Safety Science Journal | 2026, 36(4): 244-251
Public Safety and Emergency Management
Deep learning prediction for subway section passenger flow integrating physical information and snow geese optimization
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Jiahui Wan1, Xiaoxia Yang**, 1, Yuanlei Kang2, Chuang Shao3
Affiliations
  • 1School of Information and Control Engineering, Qingdao University of Technology, Qingdao Shandong 266520, China
  • 2CRRC Qingdao Sifang Co., Ltd., Qingdao Shandong 266111, China
  • 3School of Civil Engineering, Qingdao University of Technology, Qingdao Shandong 266520, China
Published: 2026-04-28 doi: 10.16265/j.cnki.issn1003-3033.2026.04.0517
Outline
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The rapid growth of passenger volume of urban rail transit and the urgent need for intelligent operation have made accurate section passenger flow prediction a key technical challenge to improve the level of dynamic scheduling and safety control. To this end, this paper innovatively integrated the physical information constraint mechanism, data-driven method and SGA, and proposed a new deep learning framework. Firstly, a physical residual term was designed and embedded into memory cells as a regulation signal, forcing the model to learn the physical laws of passenger flow while retaining the temporal characteristics of passenger flow. Secondly, a dual-objective fitness function based on physical loss and data loss was innovatively proposed to achieve further optimization of model performance while establishing a constraint mechanism. Finally, SGA was used to balance the differentiated and synergistic effects of hyperparameters in the model. Experimental results show that the constructed model exhibits good predictive performance on both the training set and the validation set. The improved fitness function can narrow the error range of the model prediction results. In the two-stage ablation experiment, the mean square error range of the proposed deep learning framework is reduced by 71.03% compared with the long short-term memory model, which verifies the synergistic enhancement of the model prediction ability by the simultaneous introduction of physical constraint mechanism and intelligent optimization algorithm.

physical information  /  snow geese algorithm (SGA)  /  subway  /  section passenger flow  /  deep learning
Jiahui Wan, Xiaoxia Yang, Yuanlei Kang, Chuang Shao. Deep learning prediction for subway section passenger flow integrating physical information and snow geese optimization[J]. China Safety Science Journal, 2026 , 36 (4) : 244 -251 . DOI: 10.16265/j.cnki.issn1003-3033.2026.04.0517
Year 2026 volume 36 Issue 4
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.04.0517
  • Receive Date:2025-10-14
  • Online Date:2026-07-08
  • Published:2026-04-28
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History
  • Received:2025-10-14
  • Revised:2025-12-20
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Affiliations
    1School of Information and Control Engineering, Qingdao University of Technology, Qingdao Shandong 266520, China
    2CRRC Qingdao Sifang Co., Ltd., Qingdao Shandong 266111, China
    3School of Civil Engineering, Qingdao University of Technology, Qingdao Shandong 266520, China
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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