Network reconstruction and prediction of mobility and interaction patterns in social environments: the Parma University Campus as a case study

Cities represent one of the most fascinating man-made complex systems. The study of the mobility and social dynamics occurring in urban areas has aroused particular interest in the scientific world. The emergence of the Covid-19 pandemic has led the scientific community to study and model social dynamics in public environments, to understand how they influence the epidemic process and to develop effective containment measures. The need to create a sustainable transport system to reduce CO2 emissions and make travel more efficient, has required the study and mathematical modelling of urban travel to determine how travel occurs. However, mathematical modelling without real application turns out to be a partial and incomplete study. The use of a data-driven approach allows mathematical models to be studied and tested in real social environments using data from widely used electronic devices. Within this framework, in this thesis we focus of data-driven processes of modeling mobility, transportation, and social dynamics in urban environments. We mainly focus on the study and analysis of the dynamics of group formation and reshuffling and user interactions in public environments, during the Covid-19 pandemic by going to evaluate the effect of containment measures. We used the empirical data from the groups to go test and demonstrate the effectiveness of contact tracing mechanisms, in particular sideward contact tracing, for suppressing epidemic spread. For the urban mobility dynamics part, we propose a model for simulation and prediction of the use of public transportation as a mode of transportation. We show the results of the data-driven models applied to the city of Parma and in particular the effect of the Scientific Campus on public transportation and the social dynamics occurring within it.