Data-Driven Analysis of Directional Rhythms and Spatial Correlates of Fare-Exempt Metro Activity Among Older Adults: A Spatially Cross-Validated Study in Chongqing

Using a data-driven framework, we examined when recorded older-adult metro activity occurs and whether detailed spatial measures predict station activity better than simple contextual variables. The data comprise 22,908,241 recorded entry and exit events from age-65+ fare-exempt cards at 263 Chongqing metro stations in March 2025, aggregated by station and clock hour. We characterized entry–exit directionality and modelled total and population-adjusted activity using older-population catchments, points of interest (POIs), metro topology, pedestrian networks, and route-level terrain. Predictive comparisons used geographically blocked cross-validation, with all preprocessing and tuning confined to the training data. Entry and exit counts showed clear temporal and spatial differences, although net inflow prediction transferred poorly to held-out areas (out-of-fold R2 = −0.099 to 0.042). Population aged 65+ and transfer status supplied most of the transferable signal (out-of-fold R2 = 0.472). POIs added little (ΔR2 = 0.007), while richer spatial representations did not improve held-out prediction. Thirty repeated geographic partitions confirmed a small average advantage for the POI-augmented model but also showed sensitivity to the partition. These findings support parsimonious station activity modelling. A complementary screening analysis defined a reproducible 45-station pool for targeted access audits.