A Digital Twin is a digital representation of physical systems, and factors real-time data for real-time decision making. This paper explores the Digital Twin concept and how it can be applied to enhance citizen-centric services in smart cities. Digital Human Twins facilitate the simulation of a digital representation of a citizen and are enriched with real-time context on orchestrated digital services – be it urban mobility or transport, social services, or wellness. The present of a Human Digital Twin helps predict any abnormal events for citizens and validates various what-if scenarios, providing the citizen with the best choice of engagement with that particular service on a mass personalized level.
Continuous learning about the citizen from past engagements, exhibited behavior, activities, feedback, preferences help to map the citizen to the most appropriate grouping called personas. Based on the persona, a further mass personalization of services thus becomes possible.