  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.
