A big-data approach to assess transport poverty: a case study of Madrid

Abstract

Transport poverty, a multifaceted issue, has garnered increasing attention in recent years. This study employs anonymized mobile phone data and GIS techniques to analyze commuting patterns, economic burdens, and spatial distribution of transport poverty. By integrating data from various sources, including mobile phone records and income statistics, the study provides insights into the relationship between transport accessibility, income levels, and social inclusion. This methodology has been used to examine a case study in Madrid's economic area. The findings underscore the importance of accessibility indicators in understanding and addressing transport poverty. Through this bottom-up data processing approach, the study demonstrates the utility of big data analytics in informing evidence-based policy interventions to promote equitable access to transportation services.