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 ${\ell }_1$-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.

统计预测模型可用于对道路交通建模，在该模型中，高维时间序列在每天结束时重新生成。由于进行了再生，因此预测是基于使用矢量自回归模型的每日模型进行的，该模型将一天中的过去观测值线性地组合在一起。由于维数高，学习算法遵循${\ell }_{\mathrm{1个}}$-对回归系数进行惩罚。在高维框架下建立了过多的风险界限，其中路段的数量与观察到的天数成无穷大。考虑到在市区观察到的浮动汽车数据，将该方法与包括神经网络在内的最新方法进行了比较。除了在预测方面具有很高的竞争力之外，它还可以识别道路网络中最决定性的部分。