Data-driven analysis of sustainable mobility and efficiency in urban and social complex systems

The analysis of large-scale data is increasingly vital for urban planning, driven by advances in AI and cloud computing. Within this framework, urban mobility plays a central role. As cities face population concentration and congestion, understanding movement patterns is essential for sustainable development. Mobility regulates access to education and work, and is deeply intertwined with social behavior: daily routines generate spatial flows, while network structures shape human interaction. Data-driven analyses thus provide a powerful lens to investigate both urban dynamics and social behavior within confined institutional settings. At the University of Parma, advanced IT systems collect diverse datasets organized in a data lake. This infrastructure allows researchers to monitor service access and address key questions: which campus areas are most visited, which pedestrian pathways are critical, and how these patterns impact academic performance or dropout risk. The answers can inform targeted policies, such as prioritizing infrastructure maintenance or designing collaborative spaces. Grounded in complex systems and statistical physics, this thesis investigates mobility and social dynamics through three complementary studies, adapting physics-inspired approaches to interdisciplinary contexts. The first project focuses on campus pedestrian mobility. Using Wi-Fi data, we reconstruct movements within a geometric network of pathways, estimating crowding and mapping flows. We quantify network entropy and robustness, assess information gain from tracking, and evaluate how traffic redistributes after removing specific paths, identifying critical infrastructure to guide maintenance. The second project utilizes campus mobility to construct a social network based on co-presence and movement simultaneity. Clustering students who frequently share spaces creates a data-driven representation of social interactions. Correlating this network with exam records shows that groups with regular class attendance achieve higher academic outcomes, while those with extracurricular-oriented mobility perform below average, proving that mobility patterns uncover social structures and guide strategies for student success. The third project develops traffic simulations on a grid to examine how traffic light timing affects the balance between cars and sustainable transport. Commuters adaptively choose modes, leading the system toward a Nash equilibrium. The model reveals a counterintuitive “traffic light paradox”: giving more green time to cars can increase congestion, as favorable signals attract more drivers whose collective delays outweigh individual gains. Modest adjustments in signal timing can thus promote sustainable transit without costly infrastructure changes. Together, these studies demonstrate the power of integrating digital traces with complex systems and statistical physics to address pressing urban and social challenges, offering actionable insights for more efficient and sustainable environments.