Where Are Potential Areas for Transit-Oriented Development (TOD)—Exploring the Demands for Built Environment for TOD Planning

Abstract

Sustainable urban growth advocates the implementation of transit-oriented development (TOD) to optimize urban spatial structure. The bilateral planning concept of TOD emphasizes the importance of discovering areas with existing TOD features but poor public transit service (potential TOD areas) and further introducing transit connectivity or conducting TOD policy in such areas to facilitate sustainable transportation. However, current studies that are devoted to discovering potential TOD areas remain scarce. In this study, we find that random forest (RF) is an optimal algorithm that can effectively identify potential TOD regions in Hong Kong. We propose an RF-mediated machine learning model (RF-TPI model) and reveal underlying mechanisms of specific indicators. After iteratively learning the typical features of TOD areas in Hong Kong, the developed RF-TPI model shows great capacity to identify potential TOD areas, with satisfactory model performances (accuracy score: 0.89, precision score: 0.81). Further investigation on manifestations of indicators by the SHapley Additive exPlanations (SHAP) interpreter demonstrates the intricate, significant nonlinear and threshold effects of distinct indicators. Conclusively, we highlight that random forest would be a prospective tool for identifying potential TOD areas to aid TOD strategy in urban sustainable endeavors.