  A statistical predictive model in which a high-dimensional time-series
regenerates at the end of each day is used to model road traffic. Due to the
regeneration, prediction is based on a daily modeling using a vector
autoregressive model that combines linearly the past observations of the day.
Due to the high-dimension, the learning algorithm follows from an
L1-penalization of the regression coefficients. Excess risk bounds are
established under the high-dimensional framework in which the number of road
sections goes to infinity with the number of observed days. Considering
floating car data observed in an urban area, the approach is compared to
state-of-the-art methods including neural networks. In addition of being highly
competitive in terms of prediction, it enables the identification of the most
determinant sections of the road network.
