The rapid urbanization and over-crowded urban environment have caused a serious public health crisis. Numerous studies have found that public green spaces can benefit human health and well-being. Therefore, a short supply or an inappropriate planning of public green spaces would exaggerate the health crisis. For all these reasons, how to create health-promoting greenways in urban areas becomes a critical and pressing challenge for urban sustainability. To address this challenge, we conducted a photograph-survey study of a greenway to examine the relationship between place preference, perceived health benefit, and environmental perception. Through a set of linear regression analysis, we found that: place preference is significantly and positively associated with six specific perceptions, including relaxation when walking alone, cheering of one’s mood, being away from daily life, traffic safety, recovery from stress, and mental fascination. Furthermore, we identified the important environmental perception elements that have significant positive or negative associations with each identified perception; these were carefully planned. This study is an initial effort to examine a critical urban land-use issue: appropriate planning of greenways in the city to promote public health and well-being. The research findings provide strong and clear guidance on planning strategies for urban greenways and shed light on future studies.

Dockless bike-sharing (DBS) plays a crucial role in solving the “last-mile” problem for metro trips. However, bike–metro transfer usage varies by time and transfer flows. This study explores the nonlinear relationship between the built environment and bike–metro transfer in Shenzhen, considering different times and transfer flows while incorporating spatial dependence to improve model accuracy. We integrated smart card records and DBS data to identify transfer trips and categorized them into four types: morning access, morning egress, evening access, and evening egress. Using random forest and gradient boosting decision tree models, we found that (1) introducing spatial lag terms significantly improved model accuracy, indicating the importance of spatial dependence in bike–metro transfer; (2) the built environment’s impact on bike–metro transfer exhibited distinct nonlinear patterns, particularly for bus stop density, house prices, commercial points of interest (POI), and cultural POI, varying by time and transfer flow; (3) SHAP value analysis further revealed the influence of urban spatial structure on bike–metro transfer, with residential and employment areas displaying different transfer patterns by time and transfer flow. Our findings underscore the importance of considering both built environment factors and spatial dependence in urban transportation planning to achieve sustainable and efficient transportation systems.