An Improved Model Combining Outlook Attention and Graph Embedding for Traffic Forecasting

Due to the highly non-linear nature of traffic data and the complex structure of road networks, traffic forecasting faces significant challenges. In this paper, we propose an improved model that combines outlook attention and graph embedding (MOAGE) for traffic forecasting, focusing on the construction of reasonable and effective spatio-temporal dependencies. Inspired by the idea of symmetry, MOAGE adopts a symmetrical encoder and decoder structure. Outlook attention blocks are important components of the encoder and decoder, consisting of spatial outlook attention and temporal outlook attention, used to model spatio-temporal dependencies in the road network. Cross attention are added to the model to reduce propagation errors. In addition, we learned the vertex representation of the graph via the node2vec algorithm and integrated the graph information into our model for a better prediction performance. Extensive experiments on two real datasets further demonstrate that the RMSE errors of the MOAGE on PEMS_BAY and METR_LA are reduced by approximately 14.6% and 12.2% for 60 min compared with the latest baseline models. Finally, the methodology used in this study will provide guidance to relevant ministries to better allocate transport resources and improve the efficiency and safety of traffic operations