基于时空依赖性和注意力机制的交通速度预测

交通流精准预测对保障公共安全和解决交通拥堵具有重要的意义,在城市交通规划、交通管理、交通控制等起着重要的作用.交通预测由于其受限制于城市路网并且随着时间动态变化,其中存在着空间依赖与时间依赖,是近些年来具有挑战性的课题之一.为了同时捕获到空间和时间上的依赖,提出了一个新的神经网络:基于注意力机制的时空图卷积网络(A-TGCN).TGCN网络模型用于捕获交通数据中的动态时空特性与相关性,采用注意力机制来增强每个A-TGCN层中关键节点的信息.通过在两组数据上的实验结果表明,A-TGCN在精度以及可解释性方面都有很好的表现.

Traffic Speed Prediction Based on Spatial-Temporal Dependency and Attention Mechanism

Accurate prediction of traffic flow is of great significance for safeguarding public safety and solving traffic congestion, and plays an important role in urban traffic planning, traffic management, and traffic control. Traffic forecasting is one of the challenging topics in recent years because it is restricted to urban road networks and changes with time, and there are spatial dependence and time dependence. In order to capture both spatial and temporal dependencies, a new neural network is proposed: A space-time map convolutional network based on the attention mechanism (A-TGCN). The TGCN network model is used to capture the dynamic spatiotemporal characteristics and correlations in traffic data, and an attention mechanism is used to enhance the information of key nodes in each A-TGCN layers. The experimental results on two sets of data show that A-TGCN has a good performance in terms of accuracy and interpretability.