Network-Scale Traffic Modeling And Forecasting With Graphical Lasso

Traffic flow forecasting is an important application domain of machine learning. How to use the information provided by adjacent links more efficiently is a key to improving the performance of Intelligent Transportation Systems (ITS). In this paper, we build a sparse graphical model for multi-link traffic flow through the Graphical Lasso (CL) algorithm and then implement the forecasting with Neural Networks. Through a large number of experiments, we find that network-scale traffic forecasting with modeling by Graphical Lasso performs much better than previous research. Traditional approaches considered the information provided by adjacent links but did not extract the information. Thus, although they improved the performance to some extent, they did not make good use of the information. Furthermore, we summarize the theoretical analysis of Graphical Lasso algorithm. From theoretical and practical points of view, we fully verify the superiority of Graphical Lasso used in modeling for multi-link traffic flow forecasting.