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（hybrid convolutional-recurrent neural network）Conv3D-GRU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（Convolutional long short-term memory, ConvLSTM）的预测结果进行比较，使用均方根误差（root mean squared error, RMSE）、平均绝对误差（mean absolute error, MAE）、决定系数R²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。

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.