Improving Road Traffic Speed Prediction Using Data Augmentation: A Deep Generative Models-based Approach

Abstract

Deep learning prediction models have emerged as the most widely used for the development of intelligent transportation systems (ITS), and their success is strongly reliant on the volume and quality of training data. However, traffic datasets are often small due to the limitations of the resources used to collect and store traffic flow data. Data Augmentation (DA) is a key method to improve the amount of the training dataset before applying a prediction model. In this paper, we demonstrate the effectiveness of data augmentation for predicting traffic speed by using a Deep Generative Model-based approach (DGM). We empirically evaluate the ability of time series-appropriate architectures to improve traffic prediction over a Train on Synthetic Test on Real(TSTR) process. A Time Series-based Generative Adversarial Network model is used to transform an original road traffic dataset into a synthetic dataset to improve traffic prediction. Experiments were carried out using the 6th Beijing and PeMS datasets to show that the transformation improves the prediction model’s accuracy using both parametric and non-parametric methods. Original datasets are compared with the generated ones using statistical analysis methods to measure the fidelity and behavior of the produced data.