Real-Time Road Traffic Forecasting Using Regime-Switching Space-Time Models and Adaptive Lasso

Smart transportation technologies require real-time traffic prediction to be both fast and scalable to full urban networks. We discuss a method that is able to meet this challenge while accounting for nonlinear traffic dynamics and space-time dependencies of traffic variables. Nonlinearity is taken into account by a union of non-overlapping linear regimes characterized by a sequence of temporal thresholds. In each regime, for each measurement location, a penalized estimation scheme, namely adaptive lasso, is implemented to perform both model selection and coefficient estimation. Both the robust to outliers least absolute deviation estimates and conventional lasso estimates are considered. The methodology is illustrated on five-minute average speed data from three road networks.