Xing Xue, Wan Le, Luo Fahui
School of Information and Control Engineering, Jilin University of Chemical Technology, Jilin, China.
PLoS One. 2025 Jul 15;20(7):e0328452. doi: 10.1371/journal.pone.0328452. eCollection 2025.
The seamless integration of shared bikes and metro systems promotes green and eco-friendly travel, yet the supply-demand imbalance of shared bikes around metro stations remains a critical challenge, making accurate demand prediction particularly crucial. Targeting metro-adjacent areas, this study proposes a method to identify shared bike trips connecting to metro usage, effectively filtering out approximately 24% of non-connecting travel records within the buffer zones. A predictive model integrating a Spatiotemporal Attention Graph Convolutional Network (STAGCN), Long Short-Term Memory (LSTM) network, and Informer is developed to forecast shared bike demand for metro connectivity. Specifically, the Informer model incorporates STAGCN to capture spatial correlations in bike demand and introduces an LSTM module to learn long- and short-term temporal dependencies. The final demand prediction is generated through a multilayer perceptron. Experiments conducted on shared bike and metro datasets in Shenzhen demonstrate that the proposed model achieves a coefficient of determination (R2) of 0.893, outperforming baseline models by 6.7% in prediction accuracy. Additionally, it exhibits lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to traditional time-series forecasting methods. The proposed demand prediction model can assist operators in optimizing the allocation of shared bike resources, which is of great significance for improving user experience.
共享单车与地铁系统的无缝整合促进了绿色环保出行，但地铁站周边共享单车的供需不平衡仍然是一个严峻挑战，因此准确的需求预测尤为关键。针对地铁站周边区域，本研究提出了一种识别与地铁使用相关的共享单车出行的方法，有效过滤了缓冲区约24%的非关联出行记录。开发了一种融合时空注意力图卷积网络（STAGCN）、长短期记忆（LSTM）网络和Informer的预测模型，以预测与地铁连通性相关的共享单车需求。具体而言，Informer模型结合STAGCN来捕捉自行车需求的空间相关性，并引入LSTM模块来学习长期和短期的时间依赖性。最终的需求预测通过多层感知器生成。在深圳的共享单车和地铁数据集上进行的实验表明，所提出的模型实现了0.893的决定系数（R2），预测准确率比基线模型高6.7%。此外，与传统时间序列预测方法相比，它表现出更低的均方根误差（RMSE）和平均绝对误差（MAE）。所提出的需求预测模型可以帮助运营商优化共享单车资源的分配，这对改善用户体验具有重要意义。
