The efficient and accurate prediction of urban travel demand, which is a hot topic in intelligent transportation research, is challenging due to its complicated spatial-temporal dependencies, dynamic nature, and uneven distribution. Most existing forecasting methods merely considered the static spatial dependencies while ignoring the influence of the diversity of dynamic demand patterns and/or uneven distribution. In this paper, we propose a traffic demand forecasting framework of a hybrid dynamic graph convolutional network (HDGCN) model to deeply capture the characteristics of urban travel demand and improve prediction accuracy. In HDGCN, traffic flow similarity graphs are designed according to the dynamic nature of travel demand, and a dynamic graph sequence is generated according to time sequence. Then, the dynamic graph convolution module and the standard graph convolution module are introduced to extract the spatial features from dynamic graphs and static graphs, respectively. Finally, the spatial features of the two components are fused and combined with the gated recurrent unit (GRU) to learn the temporal features. The efficiency and accuracy of the HDGCN model in predicting urban taxi travel demand are verified by using the taxi data from Manhattan, New York City. The modeling and comparison results demonstrate that the HDGCN model can achieve stable and effective prediction for taxi travel demand compared with the state-of-the-art baseline models. The proposed model could be used for the real-time, accurate, and efficient travel demand prediction of urban taxi and other urban transportation systems.

中文翻译：

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基于混合动态图卷积网络模型的城市出租车出行需求预测

城市出行需求的高效准确预测是智能交通研究的热点，由于其复杂的时空依赖性、动态性和分布不均等特点，具有挑战性。大多数现有的预测方法仅考虑静态空间依赖性，而忽略了动态需求模式的多样性和/或分布不均匀的影响。在本文中，我们提出了一种混合动态图卷积网络（HDGCN）模型的交通需求预测框架，以深入捕捉城市出行需求的特征并提高预测精度。在HDGCN中，根据出行需求的动态性设计交通流相似图，并根据时间序列生成动态图序列。然后，引入动态图卷积模块和标准图卷积模块，分别从动态图和静态图中提取空间特征。最后，将两个组件的空间特征融合并与门控循环单元（GRU）结合以学习时间特征。HDGCN模型在预测城市出租车出行需求方面的效率和准确性通过使用来自纽约市曼哈顿的出租车数据进行验证。建模和比较结果表明，与最先进的基线模型相比，HDGCN模型可以实现对出租车出行需求的稳定有效的预测。该模型可用于城市出租车和其他城市交通系统的实时、准确、高效的出行需求预测。