Forecasting passenger flows and headway at train level for a public transport line: Focus on atypical situations

Abstract

This paper proposes a computer vision framework based on deep learning approaches for the real-time prediction of passenger loads and train headways in a metro line in an urban transit network. The short-term prediction problem is formulated as an image completion task. The train journeys on the metro line are represented as images, with the pixels denoting the train data, including the departure location, time, and load. Metro line applicative constraints, e.g., irregular time sampling of trains and univariate space, are considered in the images. Several deep learning-based architectures are investigated, including two new architectures based on transformers. Finally, an in-depth analysis and comparison of the models is performed based on a real test case of Paris metro line 9, for which a large database was collected over three years. This allowed us to include numerous instances with atypical transport system performance indicators (e.g., strikes, lockdowns, and disruptions) and to design a methodology (based on a latent space representation of the dataset) to identify and label interesting test cases. We evaluate and demonstrate the robustness of the proposed approaches numerically and study their limits for passenger loads and headways in atypical scenarios.