This seminar is divided into two talks, discussing particular aspects of the overall theme. 

11:00 a.m.
Laura Alessandretti: Human mobility: insights from large-scale behavioural data

Large-scale behavioural data from smartphones allow us to observe how people move across cities with unprecedented resolution. These data reveal striking regularities in daily mobility, showing how activities, urban infrastructure, and social context shape where and when we travel. In this talk, I will present key empirical insights from computational human mobility research and illustrate how these findings have informed applications ranging from epidemic modelling to understanding urban segregation and transport. I will conclude by outlining current efforts toward generative models that simulate realistic mobility behaviours across cities.

11:45 a.m.
Silvia De Sojo: Gender gaps in human mobility

Gender differences in mobility have long been documented: women tend to rely more on public and flexible transport, travel shorter distances, and often combine multiple purposes within a single trip. While these insights are well established, large-scale data now allow us to explore these differences with unprecedented detail and scope. In this talk, we present findings from a large-scale smartphone dataset linking mobility traces with self-reported gender for over half a million individuals across ten countries. Using these data, we first show systematic gender differences in how people move and organize their daily travel. We then examine how these patterns relate to work routines, urban environments, and parenthood. Together, these analyses reveal how structural factors and social roles shape gendered mobility, highlighting the importance of large-scale, gender-disaggregated data for understanding behavioral inequalities and guiding the design of more inclusive cities.

About the speakers

Laura Maria Alessandretti is an Associate Professor at DTU Compute (Technical University of Denmark), where she leads research on human mobility, behavioural data science, and computational social science. Her work combines large-scale smartphone data and computational modeling to understand how people move and interact with urban environments. She co-leads the Social Complexity Lab at DTU.

Silvia De Sojo Caso is a Postdoctoral Researcher at the Technical University of Denmark (DTU) and a member of the Social Complexity Lab. Her research focuses on understanding human behavior, both online and offline, with a special emphasis on behavioral inequalities and gender differences. She combines large-scale data with methods from Complex Systems and Computational Social Science. During her PhD, she examined gender differences in mobility at scale, highlighting how work and parenthood relate to distinct patterns of movement.

Free of charge - All are welcome