STMAG: A spatial-temporal mixed attention graph-based convolution model for multi-data flow safety prediction

Abstract

Spatiotemporal safety forecasting has various applications in the neuroscience, climate and transportation domains. It is challenging due to (1) the complex spatial dependency on networks, (2) non-linear temporal dynamics with changing conditions and (3) the inherent difficulty of long-term forecasting. To address these challenges, a safety prediction model called the Spatial-Temporal Mixed Attention Graph-based Convolution model (STMAG) is proposed. Specifically, STMAG captures spatial dependency using graph convolutional networks (GCN), and temporal dependency using the sequence-to-sequence (Seq2Seq) architecture with the mixed attention mechanisms. A case study on the implementation of this model in traffic safety prediction is given as an example. Traffic safety forecasting is one canonical example of such a learning task, which is also a crucial problem to improving transportation and public safety. A number of detailed features (such as vehicle type, braking state, whether changing lanes or not) and exogenous variables (such as weather, time and road condition) are extracted from our big datasets. Finally, we conduct extensive experiments to evaluate the STMAG framework on real-world large-scale road network traffic datasets. Extensive experiments on our dataset show that the STMAG framework makes reasonably accurate predictions and significantly improves the prediction accuracy over baseline approaches.