Rail Service Quality and Regional Well-Being: Evidence from Italy

Effective and dependable transportation systems are vital for economic growth, productivity, and social welfare. While railways are used throughout the world, the wider socio-economic value of rail quality, visible in its effects on territorial inequalities and subjective well-being, is not well understood. This study seeks to address this issue through the combination of artificial intelligence, composite indicators, and transportation economics, and to quantify how perceived rail quality contributes to sustainable regional development in Italy. A large amount of user-generated social media posts were analyzed using deep learning and natural language processing (NLP) techniques to help extract perceptions on rail service quality with regard to punctuality, cleanliness, crowding, frequency, accessibility, and safety. These dimensions of perceived quality were then aggregated into a single measure using the DP2 distance-based method to create a Service Satisfaction Index (SSI) which was included in multi-level econometric models that explored the relationships between SSI, subjective wellbeing, GDP, and transport investment. Overall, the study finds that overall perceptions of rail service shape subjective well-being and are positively correlated with state and territory gross product, while a clear South-North divide in perceptions of service quality clearly underlie structural inequalities in transport infrastructure. The research highlights the importance of AI-enabled perception analysis to monitor transport performance in real time and uses rail service quality as a strategic lever for sustainable growth, territorial cohesion and social inclusion. The policy implications point to the quality-centred investment and integrated transport strategies for reducing regional inequalities and improving sustainable mobility.