Abstract Although many studies investigate the association between land use and station ridership, few examine their nonlinear and moderating relationships. Using metro smartcard data in Shenzhen, we develop a gradient boosting decision trees model to estimate the relative importance of land use variables and their threshold and moderating effects on ridership. We found that station betweenness centrality has the largest predictive power, followed by employment density and commercial floor area ratio (FAR). Results suggest that employment density, commercial FAR, and aggregate residential density should be set at 40,000 jobs/km2, 2, and 77,000 persons/km2, respectively, for maximizing ridership. The moderating effects show that population densification is more effective at terminal stations, whereas the policies intensifying nonresidential use work better at middle stations. These findings help planners prioritize land use strategies, identify effective ranges of land use metrics, and propose land use guidelines adaptive to the network position of stations.

中文翻译：

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土地利用对深圳地铁客流量的阈值和调节效应：对TOD规划的启示

摘要 尽管许多研究调查了土地利用和车站客流量之间的关系，但很少研究它们的非线性和调节关系。使用深圳的地铁智能卡数据，我们开发了一个梯度提升决策树模型来估计土地利用变量的相对重要性及其阈值和对乘客量的调节作用。我们发现车站介数中心性具有最大的预测能力，其次是就业密度和商业容积率 (FAR)。结果表明，就业密度、商业容积率和总住宅密度应分别设置为 40,000 个工作岗位/km2、2 和 77,000 人/km2，以最大限度地提高乘客量。调节效应表明终端站的人口密集化更有效，而加强非住宅使用的政策在中间站效果更好。这些发现有助于规划者优先考虑土地利用策略，确定土地利用指标的有效范围，并提出适应站点网络位置的土地利用指南。