School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
School of Transportation, Southeast University, Nanjing 211189, China.
Sensors (Basel). 2022 Aug 10;22(16):5982. doi: 10.3390/s22165982.
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.
城市出行需求的高效准确预测是智能交通研究的热点问题，由于其复杂的时空相关性、动态性和不均匀分布，具有一定的挑战性。大多数现有的预测方法仅考虑了静态的空间相关性，而忽略了动态需求模式的多样性和/或不均匀分布的影响。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，提高预测精度。在 HDGCN 中，根据出行需求的动态特性设计交通流量相似图，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，融合两个组件的空间特征，并与门控循环单元（GRU）相结合，学习时间特征。通过使用来自纽约市曼哈顿的出租车数据，验证了 HDGCN 模型在预测城市出租车出行需求方面的效率和准确性。建模和比较结果表明，与最先进的基线模型相比，HDGCN 模型可以对出租车出行需求进行稳定有效的预测。所提出的模型可用于城市出租车和其他城市交通系统的实时、准确和高效出行需求预测。
