Abstract: 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.

Key words: traffic data prediction, spatio-temporal dependency, spatio-temporal Transformer, graph neural network