Smart Service Design in Urban Rail: AI-Enhanced Blueprint, Digital Servicescape, and Passenger Experience at Jabodebek LRT Jatimulya Station

Urban rail systems are increasingly positioned as vital components of sustainable mobility, enabling modal shifts from private to public transport while reducing congestion and fostering urban growth. In Indonesia, the Jabodebek Light Rail Transit (LRT) represents a flagship project to modernize metropolitan transportation, yet its success depends not only on infrastructure but also on the quality of service design at the station level, where passengers evaluate safety, reliability, and comfort. Jatimulya Station, as a type-A terminal in the Bekasi corridor, highlights persistent challenges such as ticketing inefficiencies, overcrowding, inadequate wayfinding, and accessibility barriers, which undermine user satisfaction and expose the limitations of conventional operational frameworks. This study investigates how artificial intelligence can be integrated into service blueprinting and digital servicescape design to enhance passenger experience at Jatimulya Station. Using a qualitative approach supported by literature review, observations, interviews, and document analysis, the research employs thematic analysis to map passenger journeys, identify service encounter bottlenecks, and assess environmental factors affecting user perceptions. Findings demonstrate that AI-enhanced blueprinting enables predictive congestion management, dynamic staff allocation, and real-time adjustments, while digital servicescape innovations improve wayfinding, inclusivity, and transparency by synchronizing physical and digital touchpoints. Theoretically, this study extends established frameworks of service blueprint, servicescape, and service encounters into AI-driven contexts, while practically offering recommendations for PT Kereta Api Indonesia and policymakers to optimize station-level service delivery, improve passenger trust, and advance sustainable urban mobility.