Region-level passenger demand prediction plays an important role in the coordination of travel demand and supply in the urban public transportation system. The complex urban road network structure leads to irregular shapes and arrangements of regions, which poses a challenge for capturing the spatio-temporal correlation of demand generated in different regions. In this study, we propose a multi-community spatio-temporal graph convolutional network (MC_STGCN) framework to predict passenger demand at a multi-region level by exploring spatio-temporal correlations among regions. Specifically, the gated recurrent unit (GRU) is applied to encode the temporal correlation in regions into a vector. On the other hand, the spatial correlations among regions are encoded into two graphs through the graph convolutional network (GCN): geographically adjacent graph and functional similarity graph. Then, a prediction module based on the Louvain algorithm is used to accomplish the passenger demand prediction of multi-regions. The two real-world taxi order data collected in Shenzhen City and New York City are used in model validation and comparison. The numerical results show that the MC_STGCN model outperforms both classical time-series prediction methods and deep learning approaches. Moreover, in order to better illustrate the superiority of the proposed model, we further discuss the improvement of prediction performance though spatio-temporal correlation modeling and analyzing, the effectiveness of community detection compared with random classification of regions, and the advantages of regional level prediction compared with grid-based prediction models.

区域级旅客需求预测在城市公共交通系统的出行供需协调中起着重要作用。复杂的城市道路网络结构导致区域的形状和布局不规则，这对于捕获不同区域产生的需求的时空相关性构成了挑战。在这项研究中，我们提出了一个多社区时空图卷积网络（MC_STGCN）框架，通过探索区域之间的时空相关性来预测多区域级别的乘客需求。具体而言，门控循环单元（GRU）用于将区域中的时间相关性编码为矢量。另一方面，区域之间的空间相关性通过图卷积网络（GCN）被编码为两个图：地理上相邻的图和功能相似图。然后，使用基于Louvain算法的预测模块来完成多区域的乘客需求预测。在模型验证和比较中使用了在深圳市和纽约市收集的两个真实的出租车订单数据。数值结果表明，MC_STGCN模型优于经典的时间序列预测方法和深度学习方法。此外，为了更好地说明所提出模型的优越性，我们进一步讨论了通过时空相关建模和分析提高预测性能，与区域随机分类相比的社区检测有效性以及区域级别预测的优势与基于网格的预测模型相比。