Operationalizing public value and the travel experience with artificial intelligence: a longitudinal analysis of user sentiment at subway stations in São Paulo and Porto

Abstract

Urban mobility has emerged as a key factor in the challenge of achieving social and environmental sustainability in major Latin American metropolises such as São Paulo (Brazil), whose metro system has a high passenger density and an infrastructure that does not always meet user demands. In contrast, the Metro do Porto (Portugal) seeks to maintain high satisfaction rates in the face of challenges posed by growing tourism in the city and pressure on its system. Therefore, the methodology of this study conducts a longitudinal comparative analysis between these contexts, introducing the concepts of “Public Value” and “Travel Experience,” focusing on the evaluation of the passenger experience and sentiment analysis based on User-Generated Content (UGC) data collected from Google Maps between January 2017 and December 2025 through an automated extraction system. The analysis of the comments was conducted using Natural Language Processing (NLP) and Sentiment Analysis, employing DeepSeek-V3, a Large Language Model (AI model), to correlate users’ perceptions of the system with travel experience metrics. The results show a positive perception of both systems. It was noted that, despite the pressure caused by the growth of tourism and the problems identified with the ticket machines, the Trindade station continues to receive positive reviews, while the Sé station stands out for its efficiency and intermodal integration, even amid security crises. This analysis enables the development of safer and more inclusive metro systems, in accordance with the United Nations (UN) Sustainable Development Goals (SDGs) 9.1 and 11.2.