Image Inpainting and Deep Learning to Forecast Short-Term Train Loads

Developing an efficient short-term prediction framework for public transportation systems is of fundamental importance. This paper proposes a new image-processing-oriented methodology for the short-term prediction of train loads. First, we introduce a novel approach for representing the metro traffic by generating an image, exhibiting the spatial information of the trains running on a metro line while taking into account the irregular temporal sampling of the train loads. Second, we propose a prediction framework using deep learning methods. In particular, we build a U-net convolutional neural network, consisting of Inpainting and image-to-image translation mechanisms. We construct an image of the load predictions for different trains and stations. The framework performs a multi-step forecasting task for each station at any given time. The proposed prediction model is capable of making a global prediction for several departures on a whole metro line. Third, we benchmark our model against other prediction models using real load data collected over ten months on a Paris metro line. The comparison shows that the proposed framework is efficient compared to standard methods in image-processing prediction models. Finally, we evaluate the performance of the model in atypical operating situations (e.g., strike, incident). The results show that the performance of the model remains at acceptable levels of prediction errors in the event of metro traffic disruptions.