Probabilistic forecasting of train ticket booking demand with hierarchical correlations

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Forecasting the number of passengers using the data from ticket booking systems is critical for railway companies to manage revenue and allocate resources. The booking demand data have a unique hierarchical correlation structure that cannot be appropriately modeled by conventional time-series approaches. Therefore, this project aims to develop state-of-the-art probabilistic forecasting models for booking demand in train networks. The project will incorporate the hierarchical data correlations, train capacity, and the impact of cancellation into the model to improve forecasting accuracy. In partnership with ExPretio, the research outcomes of this project will benefit passenger transport companies in increasing revenue and reducing waste of resources. In addition, passenger travel demand can also be better matched with the optimized allocation of transport capacities.