Bike Sharing Systems play a central role in what is identified to be one of the six pillars of a Smart City: smart mobility. Motivated by a freely available dataset, we discuss the employment of two robust model-based classifiers for predicting the occurrence of situations in which a bike station is either empty or full, thus possibly creating demand loss and customer dissatisfaction. Experiments on BikeMi stations located in the central area of Milan are provided to underline the benefits of the proposed methods.
Cappozzo, A., Greselin, F., Manzi, G. (2019). Predicting and improving smart mobility: a robust model-based approach to the BikeMi BSS. In G. Arbia, S. Peluso, G. Rivellini (a cura di), Smart Statistics for Smart Applications 2019 - Book of Short papers (pp. 737-742). Milano : Pearson.
Predicting and improving smart mobility: a robust model-based approach to the BikeMi BSS
Cappozzo A.;Greselin F.;
2019
Abstract
Bike Sharing Systems play a central role in what is identified to be one of the six pillars of a Smart City: smart mobility. Motivated by a freely available dataset, we discuss the employment of two robust model-based classifiers for predicting the occurrence of situations in which a bike station is either empty or full, thus possibly creating demand loss and customer dissatisfaction. Experiments on BikeMi stations located in the central area of Milan are provided to underline the benefits of the proposed methods.
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CGM SIS 2019 Predicting and improving smart mobility A robust model-based approach to the BikeMi BSS.pdf
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