Data-driven accessibility to critical facilities using connected vehicle data: implications for future mobility and urban systems

Accurate measurement of accessibility to critical facilities is essential for equitable and efficient transportation and land-use planning, particularly in data-driven mobility systems. This study develops a novel hybrid framework to estimate travel time–based accessibility to schools and libraries using high-resolution connected vehicle (CV) data supplemented with Google Distance Matrix API to ensure complete spatial coverage. The approach captures true residential origins and is compared with conventional centroid-based methods to evaluate differences in accessibility outcomes. Results show strong agreement between CV and Google-based travel times (MAPE ≈ 10–12%), supporting the hybrid approach in areas with limited CV coverage. Centroid-based methods systematically overestimate travel times and introduce greater variability, resulting in approximately 25% of block groups changing accessibility classification, with the largest impacts observed in peripheral areas. A demographic-weighted relative accessibility burden metric is introduced to assess equity across population groups, including older adults, low-income, and minority populations. Results reveal clear spatial and demographic disparities, with higher travel burdens concentrated in peripheral regions and among vulnerable groups. A serviceability analysis further evaluates facility performance based on population served, number of associated block groups, and travel time thresholds, identifying areas with potential service gaps. Overall, the findings demonstrate that high-resolution mobility data can improve accessibility assessment and provide more accurate, policy-relevant insights for planning, resource allocation, and emergency preparedness. These insights contribute to emerging research on mobility futures by demonstrating how improved accessibility can reduce travel burdens, enhance system efficiency, and support more effective and equitable transportation planning.