College of Computer Science and Electronic Engineering, Hunan University, Changsha, China; School of Computer Science and Engineering, Nanyang Technological University, Singapore.
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
Neural Netw. 2023 May;162:340-349. doi: 10.1016/j.neunet.2023.03.009. Epub 2023 Mar 15.
With the development of social economy and smart technology, the explosive growth of vehicles has caused traffic forecasting to become a daunting challenge, especially for smart cities. Recent methods exploit graph spatial-temporal characteristics, including constructing the shared patterns of traffic data, and modeling the topological space of traffic data. However, existing methods fail to consider the spatial position information and only utilize little spatial neighborhood information. To tackle above limitation, we design a Graph Spatial-Temporal Position Recurrent Network (GSTPRN) architecture for traffic forecasting. We first construct a position graph convolution module based on self-attention and calculate the dependence strengths among the nodes to capture the spatial dependence relationship. Next, we develop approximate personalized propagation that extends the propagation range of spatial dimension information to obtain more spatial neighborhood information. Finally, we systematically integrate the position graph convolution, approximate personalized propagation and adaptive graph learning into a recurrent network (i.e. Gated Recurrent Units). Experimental evaluation on two benchmark traffic datasets demonstrates that GSTPRN is superior to the state-of-art methods.
随着社会经济和智能技术的发展，车辆的爆炸式增长使得交通预测成为一项艰巨的挑战，特别是对于智能城市而言。最近的方法利用了图时空特征，包括构建交通数据的共享模式和对交通数据的拓扑空间进行建模。然而，现有的方法未能考虑空间位置信息，仅利用了很少的空间邻域信息。为了解决上述局限性，我们设计了一种用于交通预测的图时空位置递归网络（GSTPRN）架构。我们首先构建了一个基于自注意力的位置图卷积模块，并计算节点之间的依赖强度，以捕获空间依赖关系。接下来，我们开发了近似个性化传播，将空间维度信息的传播范围扩展到获得更多的空间邻域信息。最后，我们系统地将位置图卷积、近似个性化传播和自适应图学习集成到一个递归网络（即门控循环单元）中。在两个基准交通数据集上的实验评估表明，GSTPRN 优于最先进的方法。
