Mobility-informed hospital siting for accessible and lower-carbon healthcare travel: an ensemble learning study in Tianjin, China

Healthcare facility layouts shape access to care, travel burdens, and travel-related carbon emissions. Conventional siting assessments, however, often rely on supply-side indicators without accounting for how patients actually travel. This study examined nine tertiary Grade-A hospitals in central Tianjin using 2,596 questionnaires from hospital visitors and spatial data on road networks, bus and metro stations, expressways, and points of interest. Six spatial and transport indicators were evaluated: surveyed medical-visitor density, regional service coverage, transport accessibility, distance to expressways, public transit coverage, and road network density. An ensemble framework dominated by a graph convolutional network was used to predict supply-demand relationships between respondents and hospitals and to compare scenarios in terms of service utility, travel efficiency, and lower-carbon performance. Respondents generally reported a travel tolerance of within 20 km and 60 min, while their willingness to use direct public transport declined as the number of stops increased. The ensemble achieved a reported validation accuracy above 90% under the study’s validation procedure. Among the scenarios evaluated, J1 combined medium surveyed medical-visitor density, high service coverage, high accessibility, greater distance from expressways, high public transit coverage, and medium road network density. This scenario had the lowest relative carbon-emission level. By incorporating observed mobility constraints, the framework may support more accessible and lower-carbon planning of urban public-health infrastructure. Because the analysis uses a hospital-based survey from one city, broader application requires population-based data and external validation.