Abstract In this paper, we study learning generalized driving style representations from automobile GPS trip data. We propose a novel Autoencoder Regularized deep neural Network (ARNet) and a trip encoding framework trip2vec to learn drivers’ driving styles directly from GPS records, by combining supervised and unsupervised feature learning in a unifified architecture. Experiments on a challenging driver number estimation problem and the driver identifification problem show that ARNet can learn a good generalized driving style representation: It signifificantly outperforms existing methods and alternative architectures by reaching the least estimation error on average (0.68, less than one driver) and the highest identifification accuracy (by at least 3% improvement) compared with traditional supervised learning methods