Optimizing subway-based TOD: identifying threshold effects among station-level parameters

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ABSTRACT Optimizing subway-based transit-oriented development (TOD) requires strategic alignment of transit infrastructure, land use balance, and multimodal accessibility. Shapley additive explanations (SHAP) and partial dependence plot (PDP) analyses are used to identify critical saturation thresholds for station-level TOD parameters. The results reveal significant variations in TOD effectiveness on the basis of subway network density in the urban context of five major cities in South Korea. In high-density cities, subway efficiency is primarily related to multimodal integration, particularly efficient bus-subway transfers and pedestrian connectivity. In medium-density cities, a poor land use mix negatively impacts ridership, highlighting the need for balanced station-area development. In hybrid-density cities, subway efficiency depends on optimal station exit configurations and controlled parking availability rather than multimodal connectivity alone. SHAP analysis is used to identify the key determinants of subway ridership, whereas PDP analysis is applied to determine saturation effects, revealing optimal thresholds related to station exits, parking availability, and land use mix. These thresholds can help planners avoid inefficient overinvestment. The findings emphasize the necessity of city-specific TOD strategies, indicating that uniform national policies yield suboptimal outcomes. This study provides data-driven insights that enable planners to align subway investments with regional mobility demands, ensuring sustainable urban transit development.

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  • Research Article
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  • 10.3390/su14148364
Where Are Potential Areas for Transit-Oriented Development (TOD)—Exploring the Demands for Built Environment for TOD Planning
  • Jul 8, 2022
  • Sustainability
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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.

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