基于混合卷积-递归神经网络的共享单车出入流预测
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1.昆明理工大学交通工程学院;2.昆明理工大学信息工程与自动化学院

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U484

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国家自然科学基金项目


Prediction of Shared Bicycle Inflow and Outflow Based on Conv3D-GRU Neural Network
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School of Traffic Engineering, Kunming University of Science and Technology

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    摘要:

    准确预测共享单车流量有助于优化共享单车的供需平衡,提高城市居民的出行便利性。为解决共享单车预测准确性不高以及时空特性捕捉不充分的问题,提出了一种混合卷积-递归神经网络Conv3D-GRU(Hybrid Convolutional-Recurrent Neural Network)模型,采用芝加哥2022全年共享单车数据进行实验,并与三维卷积神经网络3D-CNN(3D Convolutional Neural Network)模型和卷积长短期记忆网络ConvLSTM(Convolutional Long Short-Term Memory)的预测结果进行比较,使用均方根误差RMSE(Root Mean Squared Error)、平均绝对误差MAE(Mean Absolute Error)、决定系数R2(Coefficient of Determination)评估模型性能。实验结果表明,Conv3D-GRU相较于3D-CNN和ConvLSTM模型,在RMSE、MAE以及R2上分别提高了3.25%、4.90%、1.14%和11.94%、13.70%、2.46%,可见Conv3D-GRU模型的预测误差小,预测精度高,能够有效和可靠地适用于共享单车出入流的预测。

    Abstract:

    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 is 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.

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贾现广,刘欢,冯超琴,等. 基于混合卷积-递归神经网络的共享单车出入流预测[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-03-15
  • 最后修改日期:2024-07-02
  • 录用日期:2024-07-09
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