Access to essential services, including healthcare, veterinary care, and on-demand logistics, remains uneven across geographic and demographic populations, while existing assessment methods are often retrospective. This project proposes a Digital Twin Framework for Measuring, Predicting, and Optimizing Service Access that dynamically replicates real-world service ecosystems to support data-driven planning and decision-making. The framework will first measure current accessibility conditions by integrating multi-source data on service locations, transportation networks, population distribution, utilization patterns, and capacity constraints using advanced geospatial analytics. It will then predict future access scenarios under projected demographic shifts, infrastructure investments, policy changes, and service expansions or closures through machine learning and simulation-based forecasting models. Finally, the system will optimize strategic interventions by simulating “what-if” scenarios, such as facility relocation, capacity expansion, transportation redesign, or resource reallocation, to identify solutions that maximize accessibility, equity, and operational efficiency while minimizing cost. By transforming retrospective accessibility assessments into a continuously updated, predictive, and optimization-enabled digital twin, this project will provide planners, industry partners, and policymakers with a scalable decision-support platform to proactively reduce service disparities and enhance system resilience.
