社区街道可步行性对老年人出行活动的影响OA

Against the backdrop of rapid population aging,the influence of community street environments on older adults'travel behaviors has attracted increasing attention.Taking typical communities in the central urban area of Dalian as case studies,this research constructs a multi-dimensional response index system covering travel frequency and travel types.An interpretable machine learning framework(random forest integrated with SHAP)is employed to reveal the non-linear impacts of community street walkability factors on older adults'travel activities.The results indicate that street integration,connectivity,and the green view index significantly increase the daily travel frequency of older adults,with clear threshold effects.Regarding activity types,necessary activities are primarily supported by morphological factors such as road density and the number of intersections whereas social and health maintenance activities rely more on facility abundance and spatial fit.In addition,optimizing the green view index and the ratio of on-street parking spaces significantly promotes leisure activities among older adults.