Emergent patterns in human mobility: a statistical physics approach

The rapid development of Information and Communication Technologies (ICT) has opened up new possibilities for the realization of future smart cities. We view smart cities as rich and heterogeneous systems composed of a multitude of mi-croscopic, dynamic, and interacting entities. These range from individuals to vehicles, whose collective behavior gives rise to complex macroscopic patterns. Smart cities face numerous challenges, including the sustainability of tourism, traffic con-gestion and associated pollution, and the spread of diseases. Crucially, these issues are deeply intertwined with human mobility at various spatial and temporal scales. A central theme of this thesis is the study of emergent properties—macroscopic phenomena that arise from the interaction of microscopic agents. One such example is traffic, which emerges from the individual mobility demand, the individual behavior in the use of transport networks and from the vehicle dynamics on the road network. To address these challenges, the physics of complex systems aims to highlight universal properties of emergent phenomena using the data analysis and the statistical mechanics meth-ods. On the one hand data analysis provides the empirical basis for modeling, on the other statistical physics offers a natural framework to abstract away microscopic details and define collective macroscopic observables that capture system-wide behavior. The overarching goal of this thesis is to characterize emergent properties of mobility systems using microscopic dynamical data on individual behavior.