Many global automakers strive to develop technologies towards the next-generation of intelligent transportation systems (ITS). One of the primary goals of ITS is predicting future traffic speeds to optimize a driver's route, which can lead to not only alleviating traffic flow but also increasing user satisfaction with an ITS service. While prior studies have applied deep learning models to traffic speed prediction and improved model performance, existing models did not well capture abrupt speed changes. In this paper, we propose a novel model, named as adversarial prediction of traffic speed (APOTS), based on adversarial training, data augmentation, and hybrid deep learning modeling. Through the experiments with real traffic data provided by Hyundai Motor Company, we demonstrate that APOTS effectively learns dynamics of traffic speed changes and predicts traffic speed up to 40% higher in accuracy than existing prediction models.