Optimal structures and collective dynamics of human flows in transportation networks.

This thesis explores the dynamical and structural properties of human mobility within urban environments through the lens of complex systems and network science. Beginning with an introduction to the relevance of studying cities and human mobility, we outline our aim to investigate the interplay between transportation network properties and collective human flows. The theoretical background introduces essential concepts from network science and statistical physics, focusing on their application to spatial and transportation networks as well as urban systems. The thesis is devoted to three specific investigations. Firstly, we analyze the role of multiple pathways in defining effective network distances and their utility in predicting human mobility at diffusive scales, particularly in assessing pandemic potentials such as COVID-19 variants. Secondly, we delve into the optimization of flow-weighted transportation networks, demonstrating how network topologies can emerge from optimization processes under various constraints. We focus on a case study on the Greater London Area highlighting the integration of spatial attractiveness and traffic congestion in simulating human mobility patterns. The thesis finally explores the dynamics of out-of-routine mobility by integrating individual and collective behaviors. Leveraging large-scale datasets from US cities, we improve next-location prediction models by combining insights from individual trajectories and collective mobility dynamics. This approach is further examined in the context of novel mobility patterns influenced by COVID-19 restrictions, emphasizing the statistical properties of collective mobility near urban points of interests. Through these investigations, this thesis contributes to understanding complex urban systems and lays foundations for predictive models that integrate theoretical insights with empirical data to enhance our understanding of human mobility dynamics.