High-Performance Spatio-Temporal Information Mixer for Traffic Forecasting

Traffic forecasting is a critical task in the field of Intelligent Transportation Systems. Previous research in traffic forecasting has primarily focused on integrating Graph Neural Networks with other models. However, as the networks utilized for handling spatio-temporal features become more complex, the improvements of these researches improve slowly. In this paper, we aim to capture the intricate spatio-temporal traffic patterns in a simple and efficient manner by introducing a framework called Spatio-Temporal Information Mixer (STIM). We analyze various information of spatio-temporal data and mix them into spatio-temporal embedding, incorporating time series information, periodicity information, and spatio-temporal position-aware information. Subsequently, we use two simple backbones, based on Multilayer Perceptron (MLP) or attention mechanisms, to validate the effectiveness of the spatio-temporal embedding. The proposed method in this paper achieves state-of-the-art performance in both accuracy and speed on the four authentic real-world traffic forecasting datasets.