We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important functionality and a challenging task. Traffic data is often obtained from sensors deployed in a road network. Recent proposals on spatial-temporal graph neural networks have achieved great progress at modeling complex spatial-temporal correlations in traffic data, by modeling traffic data as a diffusion process. However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals. Unfortunately, nearly all previous works coarsely consider traffic signals entirely as the outcome of the diffusion, while neglecting the inherent signals, which impacts model performance negatively. To improve modeling performance, we propose a novel Decoupled Spatial-Temporal Framework (DSTF) that separates the diffusion and inherent traffic information in a data-driven manner, which encompasses a unique estimation gate and a residual decomposition mechanism. The separated signals can be handled subsequently by the diffusion and inherent modules separately. Further, we propose an instantiation of DSTF, Decoupled Dynamic Spatial-Temporal Graph Neural Network (D2STGNN), that captures spatial-temporal correlations and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Extensive experiments with four real-world traffic datasets demonstrate that the framework is capable of advancing the state-of-the-art.
翻译:我们都依赖流动性,而车辆运输影响着我们大多数人的日常生活。因此,预测公路网络交通状况的能力是一项重要功能和具有挑战性的任务。交通数据往往是从公路网络中部署的传感器上获得的。最近关于空间时速图神经网络的建议在模拟交通数据中复杂的时空关系方面取得了巨大进展,通过将交通数据建模作为传播过程,将交通数据建模,从而将交通数据建模为交通数据建模。然而,从直觉看,交通数据包含两种不同的隐藏时间序列信号,即传播信号和内在信号。不幸的是,几乎所有以前的工作都粗略地将交通信号完全视为传播的结果,而忽略了对模型性能产生负面影响的内在信号。为了改进模型性能,我们提出了一个新型的脱couped空间时空框架,以数据驱动的方式将传播和固有的交通信息分隔开来,其中包括一个独特的估算门和残余交通分解机制。分离的信号随后可以由传播和内在的模块分别处理。此外,我们提议对DSTF、D-D-D-D-D-S-S-S-S-S-S-C-C-C-C-C-C-SDiral-commal-I-I-S-S-S-S-S-S-Sy-Sy-Syal-Sy-Sy-Sy-Syal-Sy-Sy-Sy-S-S-Sy-Slvical-C-C-C-SD-Sl-Sl-SD-SD-SD-SD-SD-SD-SD-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-