Uncovering travel communities among older and younger adults using smart card data

Individual movements within transport networks create activity spaces that shape travel communities. However, few studies have examined the spatial structures within bus travel and the associated factors across different geographic areas, which may overlook the underlying travel patterns and characteristics within these communities. Taking one-month bus smart card data in Beijing, China as a case study, we first build spatial interaction networks for older and younger adults, and conduct analysis on various network measures. Then we detect travel communities using Leiden algorithm and further investigate the determinants for bus flows across different communities based on Poisson regression models. The findings indicate that older adults have a shorter peak interval, more localized activity spaces, lower network connectivity, and weaker interaction strength, suggesting limited mobility in bus travel compared to younger adults. The study highlights that travel duration and land use mix are important predictors for both groups regardless of geographic areas, and there are also differences in the factors influencing bus travel across various regional communities. The results of this study could better portray the mobility patterns, travel networks, activity structures, and determinants impacting bus travel flows among older and younger adults, thereby providing nuanced and efficient strategic support for urban transportation development.