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We propose an approach to forecast the short-term passenger flows of the urban rail network of Paris. Based on dynamic Bayesian networks, this approach is designed to perform even in case of incomplete data. The structure of the model is built so that the flows are predicted from their spatiotemporal neighbourhood, while the local conditional distributions are described as linear Gaussians. A first...
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