Silvia De Sojo Caso

Research areas

Mobility plays a vital role in our daily lives, encompassing activities such as commuting to and from work, running errands, and taking vacations. Individuals often display similar mobility behaviours when sharing responsibilities. For example, commuters typically travel to work during rush hour, parents need to drop their kids at school, and university students tend to go out on Friday nights.

Recent quantitative studies have identified a gender gap in human mobility, revealing distinct behaviours between men and women. Women tend to walk shorter distances, visit fewer locations and spend more time at home. However, the factors driving these differences remain unclear due to the lack of high-resolution capturing mobility behaviour and individual socio-demographics.

In this work, we will study the gender differences in mobility using a novel smartphone dataset containing multi-year GPS traces of millions of individuals worldwide, their app usage and demographics.

The goal of this project is twofold: (i) We quantify the gender gap across countries, age groups, and urban and rural areas using high-resolution data. (ii) We evaluate how socio-demographic factors, such as employment status, parenthood or the perception of safety, influence the mobility of both genders.

The diverse methods brought together by the field of Complex Systems Science are ideal for carrying out this analysis. We will resort to Econometrics, Machine Learning models, and Causal Inference methods to quantify the factors influencing the gender gap in mobility.

Understanding the gendered aspects of urban mobility can improve our ability to design urban spaces and transport solutions that account for different travelling needs and ensure equal access to opportunities.