In developing countries, aging is rapid and new towns in suburban and rural districts are emerging. However, the spatial accessibility and equity of healthcare services for older adults in new towns is rarely examined. This study is among the earliest attempts to evaluate the spatial accessibility and equity of public hospitals for older adults, using data from Songjiang District, Shanghai, China. A modified Gaussian Huff-based three-step floating catchment area (GH3SFCA) method was adopted based on the real-time travel costs of public transit, driving, cycling, and walking. The Gini coefficient and Bivariate Moran’s Index were integrated to estimate spatial equity. The results showed that the spatial accessibility of high-tier hospitals decreases from the central areas to the outskirts for older adults in Songjiang. Meanwhile, the accessibility of low-tier hospitals varies substantially across areas. Although the low-tier hospitals are distributed evenly, their Gini coefficient showed less equitable spatial accessibility than the high-tier hospitals. Furthermore, driving and cycling lead to more equitable spatial accessibility than public transit or walking. Finally, communities with a low-supply–high-demand mismatch for public hospitals were suggested to be improved preferentially. These findings will facilitate planning strategies for public hospitals for older adults in developing new towns.

Providing age-friendly metro service substantially enhances the elderly’s mobility and well-being. Despite recent progress in user profiling and mobility prediction, the prediction of the elderly’s metro travel patterns remains limited. To fill this gap, this study proposes a framework integrating user profiling and knowledge graph embedding to predict the elderly’s activity types at metro trip destinations, utilizing 180,143 smart card records and 885,072 points of interest (POI) records from Chongqing, China in 2019. First, an elderly metro travel profile (EMTP) tag system is developed to capture the elderly’s spatiotemporal metro travel behaviors and preferences. Subsequently, an elderly metro travel knowledge graph (EMTKG) is constructed to support semantic reasoning, transforming the activity types prediction problem into a knowledge graph completion problem. To solve the completion problem, the Temporal and Non-Temporal ComplEx (TNTComplEx) model is introduced to embed entities and relations into a complex vector space and distinguish between time-sensitive and time-insensitive behavioral patterns. Fact plausibility within the graph is evaluated by a scoring function. Numerical experiments validate that the proposed model outperforms the best-performing baselines by 13.37% higher Accuracy@1 and 52.40% faster training time per epoch, and ablation studies further confirm component effectiveness. This study provides an enlightening and scalable approach for enhancing age-friendly metro system service.