The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional network (STDCN) model. First, the pickup and drop-off locations are modeled into a directed spatio-temporal graph as input to the model. The correlation between each node is calculated as a unified edge weight based on the gray relational analysis. Then, the STDCN is used for dynamic spatio-temporal feature extraction. Finally, the edge-cloud collaboration framework is adopted to recommend local taxi pickup locations in real-time. The experimental results show that the proposed method is better than competing methods in terms of effectiveness and efficiency, and it shows good industrial conversion application prospects.

中文翻译：

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基于时空有向图卷积网络的出租车上车位置推荐

出租车上车地点的推荐对司机高效载客起着重要作用。此外，车联网的出现为其提供了技术支持。然而，现有的推荐方法不能很好地和实时地对动态全球定位系统信息进行建模。在本文中，我们提出了一种时空有向图卷积网络（STDCN）模型。首先，将上车和下车位置建模为有向时空图，作为模型的输入。基于灰色关联分析，将每个节点之间的相关性计算为统一的边权重。然后，STDCN 用于动态时空特征提取。最后，采用边云协同框架，实时推荐当地出租车上车地点。