Zhou Chaoran, Hu Jiahao, Zhang Xin, Li Zerui, Yang Kaicheng
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.
PeerJ Comput Sci. 2024 Oct 15;10:e2391. doi: 10.7717/peerj-cs.2391. eCollection 2024.
Shared bikes, as an eco-friendly transport mode, facilitate short commutes for urban dwellers and help alleviate traffic. However, the prevalent station-based strategy for bike placements often overlooks urban zones, cycling patterns, and more, resulting in underutilized bikes. To address this, we introduce the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model, leveraging multi-source data and Spatio-Temporal Graph Convolutional Networks (STGCN). This model predicts spatial user demand for bikes between stations by constructing a spatial demand graph, accounting for geographical influences. For precision, ST-BDP integrates an attention-based graph convolutional network for station demand graph's temporal-spatial features, and a sequential convolutional network for multi-source data (., weather, time). In real dataset, experimental results show that ST-BDP has excellent performance with mean absolute error (MAE) = 1.62, mean absolute percentage error (MAPE) = 15.82%, symmetric mean absolute percentage error (SMAPE) = 16.14%, and root mean square error (RMSE) = 2.36, outperforming the baseline techniques. This highlights its predictive accuracy and potential to guide future bike-sharing policies.
共享单车作为一种环保的交通方式，为城市居民的短途通勤提供了便利，并有助于缓解交通拥堵。然而，普遍采用的基于站点的自行车投放策略往往忽视了城市区域、骑行模式等因素，导致自行车利用率低下。为了解决这一问题，我们引入了时空共享单车需求预测（ST-BDP）模型，该模型利用多源数据和时空图卷积网络（STGCN）。该模型通过构建空间需求图来预测站点之间用户对自行车的空间需求，同时考虑地理因素的影响。为了提高预测精度，ST-BDP集成了一个基于注意力的图卷积网络来处理站点需求图的时空特征，以及一个序列卷积网络来处理多源数据（如天气、时间）。在真实数据集上的实验结果表明，ST-BDP具有出色的性能，平均绝对误差（MAE）=1.62，平均绝对百分比误差（MAPE）=15.82%，对称平均绝对百分比误差（SMAPE）=16.14%，均方根误差（RMSE）=2.36，优于基线技术。这突出了其预测准确性以及指导未来共享单车政策的潜力。
