Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks
This work addresses traffic flow prediction for intelligent transportation systems, but it is incremental as it builds on existing methods like graphical lasso and neural networks.
The paper tackled short-term traffic flow forecasting at network scale by proposing single-link and multi-link models combined with single-task and multi-task learning, resulting in improved experimental efficiency and prediction accuracy, with a new multi-link single-task approach using graphical lasso and neural networks.
Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper does a lot of research on network-scale modeling and forecasting of short-term traffic flows. Firstly, we propose the concepts of single-link and multi-link models of traffic flow forecasting. Secondly, we construct four prediction models by combining the two models with single-task learning and multi-task learning. The combination of the multi-link model and multi-task learning not only improves the experimental efficiency but also the prediction accuracy. Moreover, a new multi-link single-task approach that combines graphical lasso (GL) with neural network (NN) is proposed. GL provides a general methodology for solving problems involving lots of variables. Using L1 regularization, GL builds a sparse graphical model making use of the sparse inverse covariance matrix. In addition, Gaussian process regression (GPR) is a classic regression algorithm in Bayesian machine learning. Although there is wide research on GPR, there are few applications of GPR in traffic flow forecasting. In this paper, we apply GPR to traffic flow forecasting and show its potential. Through sufficient experiments, we compare all of the proposed approaches and make an overall assessment at last.