State Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Front Public Health. 2024 Nov 4;12:1469578. doi: 10.3389/fpubh.2024.1469578. eCollection 2024.
The accelerated motorization has brought a series of environmental concerns and damaged public environmental health by causing severe air and noise pollution. The advocate of urban rail transit system such as metro is effective to reduce the private car dependence and alleviate associated environmental outcomes. Meanwhile, the increased metro usage can also benefit public and individual health by facilitating physical activities such as walking or cycling to the metro station. Therefore, promoting metro usage by discovering the nonlinear associations between the built environment and metro ridership is critical for the government to benefit public health, while most studies ignored the non-linear and threshold effects of built environment on weekend metro usage.
Using multi-source datasets in Shanghai, this study applies Gradient Boosting Decision Trees (GBDT), a nonlinear machine learning approach to estimate the non-linear and threshold effects of the built environment on weekend metro ridership.
Results show that land use mixture, distance to CBD, number of bus line, employment density and rooftop density are top five most important variables by both relative importance analysis and Shapley additive explanations (SHAP) values. Employment density and distance to city center are top five important variables by feature importance. According to the Partial Dependence Plots (PDPs), every built environment variable shows non-linear impacts on weekend metro ridership, while most of them have certain effective ranges to facilitate the metro usage. Maximum weekend ridership occurs when land use mixture entropy index is less than 0.7, number of bus lines reaches 35, rooftop density reaches 0.25, and number of bus stops reaches 10.
Research findings can not only help government the non-linear and threshold effects of the built environment in planning practice, but also benefit public health by providing practical guidance for policymakers to increase weekend metro usage with station-level built environment optimization.
快速的机动化带来了一系列环境问题，通过造成严重的空气和噪声污染，破坏了公共环境卫生。地铁等城市轨道交通系统的倡导者被证明可以有效减少对私家车的依赖，并缓解相关的环境后果。同时，增加地铁的使用也可以通过促进步行或骑自行车去地铁站等活动来促进公众和个人健康。因此，通过发现城市建成环境与地铁出行量之间的非线性关系来促进地铁使用，对于政府造福公众健康至关重要，而大多数研究忽略了建成环境对周末地铁出行的非线性和阈值效应。
本研究使用上海的多源数据集，应用梯度提升决策树（GBDT），一种非线性机器学习方法，来估计城市建成环境对周末地铁出行量的非线性和阈值效应。
结果表明，土地利用混合度、到 CBD 的距离、公交线路数量、就业密度和屋顶密度是通过相对重要性分析和 Shapley 加性解释（SHAP）值确定的前五个最重要的变量。就业密度和到市中心的距离是通过特征重要性确定的前五个重要变量。根据偏部分依赖图（PDPs），每个建成环境变量对周末地铁出行量都有非线性影响，而大多数变量都有一定的有效范围来促进地铁使用。当土地利用混合度熵指数小于 0.7、公交线路数量达到 35、屋顶密度达到 0.25、公交车站数量达到 10 时，周末出行量最大。
研究结果不仅可以帮助政府规划实践中考虑建成环境的非线性和阈值效应，还可以为政策制定者提供实用的指导，通过优化地铁站周边的建成环境来增加周末地铁使用量，从而造福公众健康。
