Abstract: Traffic flow prediction plays an incredibly important role in intelligent transportation systems.Accurate traffic flow prediction can not only benefit transportation management but also provide appropriate travel plans for people.However,it is very challenging and the main difficulty lies in how to capture the complex spatial and temporal dependencies.In recent years,deep learning methods,mainly based on convolutional neural networks,have been successfully applied to traffic forecasting tasks.However,convolutional neural networks mainly focus on the extraction and integration of spatial features in data,so it is difficult to fully explore the complex spatio-temporal dependencies.Moreover,single-layer convolutional networks can only capture the local spatial dependencies,it is necessary to stack multiple layers of convolutional networks to capture the global spatial dependencies,which will slow down the convergence speed of the whole network model training.To solve these problems,a global-aware spatio-temporal network model(called ST-WaveMLP) for traffic prediction is proposed,which mainly employs a multi-layer perceptron based repeatable structure ST-WaveBlock to capture the complex spatio-temporal dependencies.ST-WaveBlock has an excellent spatio-temporal representation learning capability,often using only 2～4 ST-WaveBlock stacks to effectively capture the spatio-temporal dependencies in the data.Finally,the experimental validation on four real traffic flow datasets shows that ST-WaveMLP has better convergence and better prediction accuracy,with a relative improvement of up to 9.57% in prediction accuracy and up to 30.6% in model convergence speed compared to the previous best method.

Key words: Traffic flow prediction, Spatio-Temporal dependencies, Spatio-Temporal deep learning, Spatio-Temporal data mining