Bridging Passenger Clustering and User Experience Personalization in Long-Distance Railway Systems: A Rapid Literature Review

The rapid advancement of data-driven technologies in transportation presents new opportunities to enhance long-distance railway services. However, despite the widespread use of clustering techniques for passenger segmentation and the growing importance of user experience (UX) design, these domains remain largely disconnected in current research and practice. This study addresses this gap through a Rapid Literature Review (RLR) of 341 peer-reviewed articles published between 2019 and 2024 in the Scopus database. The selected studies were analyzed using keyword co-occurrence and thematic mapping and further classified into four passenger-type clusters: routine commuters, family travelers, budget-oriented passengers, and elderly or special-needs users. Thematic analysis identified four major research areas: UX and digital interaction, deep learning and smart mobility, intelligent public transport systems, and autonomous transport. The findings reveal a significant methodological divide: clustering is primarily applied for system optimization, while UX research focuses on interaction design, with limited integration between the two. To address this, the study proposes a conceptual framework that links clustering outputs to UX adaptation strategies for personalized railway services. This work contributes to the field of smart mobility by integrating behavioral analytics with human-centered design, offering a foundation for more adaptive, inclusive, and user-responsive railway systems. Future research is encouraged to validate the proposed framework through empirical studies and prototype development.