Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks
Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks
复制标题
基于时空图卷积网络的公共自行车共享项目需求预测
DOI：
10.1007/s11042-020-08803-y
复制
发表时间：
2020-03-13
影响因子：
3.6
通讯作者：
Ni, Anning
中科院分区：
文献类型：
--
作者：
Xiao, Guangnian;Wang, Ruinan;Ni, Anning
公共自行车共享（PBS）项目的运营再次引起关注，因为自由浮动的自行车共享项目遇到了许多问题。这些问题包括恶意损坏、盗窃、混乱的停车、大规模赤字和破产。短期需求预测是公共广播系统成功运行的关键。在这项研究中，我们使用一种新的时空图卷积网络（STGCN）来预测拾取/返回需求，通过探索潜在的信息，从多视图数据。我们应用图卷积神经网络（CNN）来表示基于地理信息系统数据表示的码头位置的空间依赖性。此外，我们使用门控CNN来表示时间依赖性，根据时间序列数据表示取/还车需求。使用温岭PBS项目一个月的多视角数据对STGCN和三个基于递归神经网络（RNN）的竞争者进行了训练和验证。基于RNN的竞争对手包括SimpleRNN，长短期记忆和门控递归单元。结果表明，STGCN实现了更高的预测精度相比，它的竞争对手。虽然STGCN与SimpleRNN相比消耗了更长的训练时间，但它需要最少数量的epoch来实现收敛精度。STGCN中的完整CNN结构可以有效地解决PBS需求预测的空间和时间依赖性。
The operation of public bike sharing (PBS) programs has attracted attention again due to numerous problems encountered by free-floating bike sharing programs. These problems include malicious damage, theft, chaotic parking, large-scale deficit and bankruptcy. The short-time demand prediction is a key issue for the successful operation of PBS programs. In this study, we use a novel spatio-temporal graph convolutional network (STGCN) to predict the picking up/returning demand by exploring potential information from multi-view data. We apply graph convolutional neural networks (CNNs) to represent the spatial dependency based on the geographic information system data denoting the location of docks. Moreover, we use gated CNNs to denote the temporal dependency according to the time-series data representing the demand for picking up/returning public bikes. The STGCN and three recurrent neural network (RNN)-based competitors are trained and validated using the multi-view data from the Wenling PBS program for one month. The RNN-based competitors consist of the SimpleRNN, long short term memory and gated recurrent unit. Results show that the STGCN achieves higher prediction accuracy compared with its competitors. Although the STGCN consumes a longer training time compared with the SimpleRNN, it requires a minimal number of epochs to achieve convergence precision. The complete CNN structure in the STGCN can effectively address the spatial and temporal dependencies for PBS demand prediction.