首页|站域建成环境与地铁客流量的非线性关系和协同效应——可解释机器学习分析

站域建成环境与地铁客流量的非线性关系和协同效应——可解释机器学习分析

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为了量化站域建成环境与地铁客流量的复杂关联效应,运用公共交通刷卡、手机信令、POI(point of interest)等多源大数据,采用可解释机器学习方法(融合随机森林和SHAP(Shapley additive explanations)模型),对站域建成环境变量与成都地铁客流量之间的非线性关系以及变量之间的协同效应进行实证研究.研究结果表明:对地铁客流量影响最大的3个建成环境变量是容积率、就业密度和道路密度.SHAP模型分析进一步揭示了站域建成环境对地铁客流量的阈值效应以及建成环境变量之间的协同效应.上述发现为以公共交通为导向的城市发展(transit-oriented development,TOD)规划和实践提供了理论支持和政策启示.
Nonlinear and Synergistic Effects of Station-area Built Environments on Metro Ridership:A Shapley Additive Explanations(SHAP)Analysis
This study uses multi-source big data(e.g.,metro card transactions,mobile phone signaling,and points of interest(POIs))and interpretable machine learning methods(integrating random forest and Shapley additive explanations(SHAP)models)to investigate the nonlinear relationship between station-area built environments and Chengdu Metro ridership as well as the synergistic effects among built environment variables.The results indicate that the three most important built environment determinants of metro ridership are the floor area ratio,employment density,and road density.Moreover,the SHAP model results reveal the threshold and synergistic effects of the station-area built environment variables on metro ridership.These findings provide theoretical support and policy insights for transit-oriented developmental(TOD)planning and practice.

urban rail transittransit-oriented development(TOD)built environmentsynergistic effectnonlinearitymachine learning

汪雨菲、杨皓森、喻冰洁、付飞、杨林川

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电子科技大学经济与管理学院,成都 611731

西南交通大学建筑学院,成都 611756

城市轨道交通 TOD 建成环境 协同效应 非线性 机器学习

国家自然科学基金项目

52278080

2024

都市快轨交通
北京交通大学,北京城建设计研究总院有限责任公司

都市快轨交通

CSTPCD北大核心
影响因子:0.785
ISSN:1672-6073
年,卷(期):2024.37(2)
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