Thanks to the development of increasingly sophisticated machine-learning techniques, it is possible to improve predictions of a particular phenomenon. In this paper, after analyzing data relating to the mobility habits of University of Foggia (UniFG) community members, we apply logistic regression and cross validation to determine the information that is missing in the dataset (so-called imputation process). Our goal is to make it possible to obtain the missing information that can be useful for calculating sustainability indicators and that allow the UniFG Rectorate to improve its sustainable mobility policies by encouraging methods that are as appropriate as possible to the users’ needs.

Machine Learning and Sustainable Mobility: The Case of the University of Foggia (Italy)

Giulio Mario Cappelletti;Luca Grilli
;
Carlo Russo;
2022

Abstract

Thanks to the development of increasingly sophisticated machine-learning techniques, it is possible to improve predictions of a particular phenomenon. In this paper, after analyzing data relating to the mobility habits of University of Foggia (UniFG) community members, we apply logistic regression and cross validation to determine the information that is missing in the dataset (so-called imputation process). Our goal is to make it possible to obtain the missing information that can be useful for calculating sustainability indicators and that allow the UniFG Rectorate to improve its sustainable mobility policies by encouraging methods that are as appropriate as possible to the users’ needs.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11369/421267
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