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

Thanks to the development of increasingly sophisticated machine-learning techniques, it is possible to improve predictions of a certain phenomenon. In this paper, after having analyzed data relating to the mobility habits of University of Foggia (UniFG) community members and deter- mined their emissions of pollutants, we applied machine-learning techniques to these data to estimate the quantities of pollutants (in a certain time period) produced by new subjects not present in the data sets, using very little information. In this way, we developed a method that the university could apply to inform new students about what their emissions of pollutants could be in the near future, through several easily obtainable features. This method could allow the UniFG Rectorate to improve its sustainable mobility policies by encouraging the use of methods that are as appropriate as possible to the users’ needs. In addition, any public/private organization outside the academic environment can use the method, due to the need for little information.