Traffic prediction is the cornerstone of an intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road networks. Meanwhile, most Recurrent Neural Network (RNN) based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this paper, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate a dynamic graph, which is integrated with a pre-defined static graph. As far as we know, we are the first to employ a generation method to model fine topology of dynamic graph at each time step. Further, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently.
翻译:交通量预测是智能运输系统的基石。 准确的交通量预测对于智能城市的应用至关重要, 即智能交通管理和城市规划。 虽然提出了多种方法用于时空空间建模, 但它们忽略了道路网络各地点相互关系的动态特征。 同时, 大部分基于神经网络( RNN) 的经常性工程由于经常运行, 效率不够。 此外, 在同一数据集的不同方法之间严重缺乏公平的比较。 为了应对上述挑战, 我们在本文件中提出一个新的交通量预测框架, 名为动态图表变动经常网( DGCRN )。 在 DGCRN 中, 超网络的设计旨在利用节点属性的动态特征, 并提取动态过滤器的参数。 我们过滤节点嵌嵌, 然后用它们生成一个动态图表, 与一个预先定义的固定图相结合。 我们知道, 我们首先使用新一代方法, 来为每步一步的动态图表建模精细的动态图表( DCR) 。 此外, 超网络的设计旨在从节点属性属性属性特性和动态特征上获取动态特征特征特征特征, 我们使用一个培训战略, 来限制标准化模型 。 。 最后, 升级 升级 升级 数据库 数据库 升级 升级 升级 升级 数据库 升级 升级 数据库 升级 升级 升级 升级 数据库 升级 升级 升级 升级 升级 升级 升级 数据库 升级 升级 升级 数据库 升级 升级 升级 升级 升级 升级 数据库 升级 升级 升级 升级 。