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Prediction of Shared Bicycle Inflow and Outflow Based on Conv3D-GRU Neural Network
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Xian-guang JIA1, Huan LIU1, Chao-qin FENG1, Ying-ying LÜ2, *
Science Technology and Engineering | 2025, 25(5) : 2127 - 2134
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Science Technology and Engineering | 2025, 25(5): 2127-2134
• Papers·Traffics and Transportations •
Prediction of Shared Bicycle Inflow and Outflow Based on Conv3D-GRU Neural Network
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Xian-guang JIA1, Huan LIU1, Chao-qin FENG1, Ying-ying LÜ2, *
Affiliations
  • 1 School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
  • 2 School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Published: 2025-02-18 doi: 10.12404/j.issn.1671-1815.2401851
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Accurately predicting bike-sharing flow is essential for optimizing the supply-demand balance of shared bikes and enhancing urban residents’ travel convenience. To address the issues of low prediction accuracy and insufficient capture of spatiotemporal characteristics in bike-sharing flow prediction, a hybrid convolutional-recurrent neural network (Conv3D-GRU) model was proposed. Using Chicago’s 2022 full-year bike-sharing data, experiments were conducted, and the results were compared with those of the 3D convolutional neural network (3D-CNN) model and the convolutional long short-term memory (ConvLSTM) model. The model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). Experimental results show that compared with the 3D-CNN and ConvLSTM models, Conv3D-GRU is improved by 3.25%, 4.90%, 1.14% and 11.94%, 13.70% and 2.46% on RMSE, MAE and R2, respectively. This demonstrates that the Conv3D-GRU model has lower prediction errors and higher prediction accuracy, making it an effective and reliable approach for forecasting bike-sharing inflow and outflow.

urban transportation  /  access flow prediction  /  Conv3D-GRU  /  bicycle sharing  /  spatio-temporal properties
Xian-guang JIA, Huan LIU, Chao-qin FENG, Ying-ying LÜ. Prediction of Shared Bicycle Inflow and Outflow Based on Conv3D-GRU Neural Network[J]. Science Technology and Engineering, 2025 , 25 (5) : 2127 -2134 . DOI: 10.12404/j.issn.1671-1815.2401851
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Year 2025 volume 25 Issue 5
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Article Info
doi: 10.12404/j.issn.1671-1815.2401851
  • Receive Date：2024-03-15
  • Online Date：2025-07-29
  • Published：2025-02-18
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  • Received：2024-03-15
  • Revised：2024-11-19
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Affiliations
    1 School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
    2 School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
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表12种不同金属材料的力学参数
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Family		 属数
Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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