A coupled multi-factor synergistic graph network model for traffic prediction

Abstract

Abstract Since only the spatio-temporal structure itself is considered in the current traffic prediction process, and there is a lack of multi-factor information about the scene, which leads to problems such as lack of universality and authenticity in the spatio-temporal scene, we proposed a coupled multi-factor synergistic graph network (CM-SGN). For this network, this paper firstly adopts a fusion mechanism to fuse traffic features with coupled scene features, constructs a complex feature correlation matrix, and enhances the multi-feature fusion effect of spatio-temporal scenes; secondly, it introduces a spatio-temporal feature network constrained by an attention mechanism to ensure the spatio-temporal stability of the fused features; and lastly, it reduces the sensitivity of the scene-traffic interdependent targets by using the regression layer combinations of the loss function to improve the prediction generalization capability. Experiments were conducted on two real datasets. Experiments were conducted on two real datasets, and the results show that the model proposed in this paper meets the complex spatio-temporal scenarios in traffic prediction, effectively captures the intrinsic spatio-temporal dependencies, and has good stability and generalization ability.