Probabilistic Forecasts of Bike-Sharing Systems for Journey Planning

Abstract : We study the problem of making forecasts about the future availability of bicycles in stations of a bike-sharing system (BSS). This is relevant in order to make recommendations guaranteeing that the probability that a user will be able to make a journey is sufficiently high. To do this we use probabilistic predictions obtained from a queuing theoretical time-inhomogeneous model of a BSS. The model is parametrized and successfully validated using historical data from the Vélib ' BSS of the City of Paris. We develop a critique of the standard root-mean-square-error (RMSE), commonly adopted in the bike-sharing research as an index of the prediction accuracy, because it does not account for the stochasticity inherent in the real system. Instead we introduce a new metric based on scoring rules. We evaluate the average score of our model against classical predictors used in the literature. We show that these are outperformed by our model for prediction horizons of up to a few hours. We also discuss that, in general, measuring the current number of available bikes is only relevant for prediction horizons of up to few hours.
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Nicolas Gast, Guillaume Massonnet, Daniël Reijsbergen, Mirco Tribastone. Probabilistic Forecasts of Bike-Sharing Systems for Journey Planning. The 24th ACM International Conference on Information and Knowledge Management (CIKM 2015), Oct 2015, Melbourne, Australia. ⟨10.1145/2806416.2806569⟩. ⟨hal-01185840⟩

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