Applying generative adversarial networks to generate synthetic train trip data for train delay prediction

This paper examines the possibilities of creating synthetic train trip data with Generative Adversarial Networks (GANs). A real data set from Deutsche Bahn is enhanced with synthetic data created by using a Conditional Wasserstein Generative Adversarial Network (CWGAN). The synthetic data is analyzed and compared with the original data using statistical methods as well as machine learning models. The results show that the synthetic data is very similar to the original data in terms of data structure and dependencies, but at the same time contains enough noise to not just copy already existing instances. To analyze and measure the quality of the synthetic data, different supervised machine learning models are trained to predict the change of delay of trains at a specific station based on the arrival delays of other trains at that station. These models are then each trained once using the real data and once using the real data enhanced by synthetic data. All models are evaluated using a test set containing only real data that was not used to train the models. The results show that the R2 value of delay predictions increases significantly when using the enhanced data set. In particular, neural network-based models can benefit from the larger amount of input data. The proposed approach of generating synthetic train trip data with a CWGAN can also be applied to various other railway data analysis projects that require a large amount of input data. In addition, the presented approach is particularly interesting because, unlike most GAN approaches discussed in current literature, the data basis contains numerical data and not image data.