基于时空Transformer的多空间尺度交通预测模型

Multi-spatial scale traffic prediction model based on spatio-temporal Transformer

准确的交通预测对提高智能交通系统的效率至关重要。交通系统的空间依赖不仅体现在道路的相连关系上,更重要的是由道路属性、区域功能等因素形成的隐藏空间依赖。另外,交通数据之间的时间依赖具有严格的相对位置关系,忽略这一问题将难以实现准确的交通预测。为了解决这些问题,提出了一种基于时空Transformer的多空间尺度交通预测模型(MSS-STT)。MSS-STT使用多个特定的Transformer网络对不同的空间尺度建模,以捕捉隐藏空间依赖,同时使用图卷积网络来学习静态空间特征。接着,使用门控机制将不同空间尺度的空间依赖与静态空间特征根据各自对预测的重要性进行融合。最后,根据时间序列中不同相对位置对预测的不同贡献来提取不同的时间依赖关系。在PeMS数据集上的实验结果表明,MSS-STT优于最先进的基线。

Accurate traffic prediction is crucial for improving the efficiency of intelligent transportation systems.The spatial dependence of the transportation system is not only reflected in the connectivity of roads,but more importantly,in the hidden spatial dependence formed by factors such as road attributes and regional functions.In addition,the time dependence between traffic data has a strict relative positional relationship,and ignoring this issue will make it difficult to achieve accurate traffic prediction.To address these issues,a multi-spatial scale traffic prediction model based on spatio-temporal Transformer(MSS-STT)is proposed.MSS-STT uses multiple specific Transformer networks to model different spatial scales to capture hidden spatial dependencies,while using graph convolutional networks to learn static spatial features.Then,a gating mechanism is used to fuse spatial dependencies and static spatial features at different spatial scales based on their respective importance for prediction.Finally,different time dependencies are extracted according to the different contributions of different relative positions in the time series to the prediction.The experimental results on PeMS dataset indicate that MSS-STT outperforms state-of-the-art baselines.