Multi-scale spatio-temporal graph neural network for urban traffic flow prediction

Abstract

Abstract The Urban traffic flow is affected by both internal supply and demand changes and external random disturbances, and during its continuous spatiotemporal propagation, these factors overlap with each other, presenting a highly non-linear and complex spatiotemporal pattern, which poses a huge challenge to traffic flow prediction. In response to the above challenges, this paper proposes a novel Spatio-Temporal Graph neural network with Multi-timeScale (abbreviated as STGMS). In STGMS, a multi-timescale feature decomposition strategy was designed to decompose the traffic flow into signals at multiple timescales and residuals. A unified spatio-temporal feature encoding module was designed to integrate the spatiotemporal features of traffic flow and the interaction features of multi-timescale traffic flows. Finally, the mapping from the multi-timescale spatiotemporal feature encoding to the future traffic flow was learned. We conducted numerous experiments on four real-world datasets and compared them with eleven baseline models from the past three years. The results show that the performance of our model outperforms the current state-of-the-art baseline models. On the four datasets, the average improvement rates of the three prediction accuracy metrics, namely the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), reach 17.69%, 15.65%, and 10.30% respectively.