量化城市轨道交通服务可靠性的因果运营决定因素：来自面板双/去偏机器学习的证据
Quantifying the Causal Operational Determinants of Service Reliability in Urban Rail Transit: Evidence from Panel Double/Debiased Machine Learning
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中文总结 AI 辅助
本研究采用面板双/去偏机器学习分析46家国际地铁运营商数据，量化了需求压力、服务供给等运营因素对地铁可靠性的因果效应，发现供需匹配度对可靠性的关键影响。
中文摘要 AI 辅助
城市轨道交通可靠性是衡量系统性能的关键指标，但由于影响因素高维且相互关联，其因果决定因素仍未得到充分量化。本研究利用CoMET基准数据库，调查了1994年至2024年间46家国际地铁运营商的可靠性模式，纳入了涵盖技术、运营、财务、环境和宏观经济条件的90多个候选变量。基于领域知识、文献综合和变量构建，设计了四个运营决定因素以捕捉三种机制：需求压力、服务供给以及供需失衡，其余变量则在理论适用的情况下被筛选并作为混杂因素纳入。本研究将适配面板数据的双/去偏机器学习（Double/Debiased Machine Learning, DML）引入地铁可靠性分析，以量化这些决定因素在复杂非线性关系下的净因果效应。该框架将灵活的机器学习与面板固定效应或随机效应（算子内时间变化）相结合，减少了高维混杂、模型误设和未观测到的运营商异质性带来的偏差。结果确定了三种不同的运营机制：更高的乘客需求强度使事故率增加0.38%（p<0.001）；在供给侧，更高的车队供给充足性和基于车辆的运营强度分别使事故率降低0.52%（p<0.05）和0.80%（p<0.01）；反映需求与可用供给失衡的运力利用率使事故率增加0.49%（p<0.001）。这些发现表明，地铁可靠性不仅取决于单独的需求或供给水平，还取决于服务供给是否与乘客需求同步。
英文摘要
Urban rail transit reliability is a critical measure of system performance, yet its causal determinants remain poorly quantified due to high-dimensional and interdependent influencing factors. This study investigates reliability patterns across 46 international metro operators between 1994 and 2024 using the CoMET benchmarking database, incorporating more than 90 candidate variables spanning technical, operational, financial, environmental, and macroeconomic conditions. Based on domain knowledge, literature synthesis, and variable construction, four operational determinants are designed to capture three mechanisms: demand pressure, service supply, and demand-supply imbalance, while the remaining variables are screened and incorporated as confounders where theoretically appropriate. Double/Debiased Machine Learning (DML) adapted for panel data is introduced to urban rail reliability analysis to quantify the net causal effects of these determinants under complex and nonlinear relationships. The framework combines flexible machine learning with panel fixed or random effects within-operator temporal variation, reducing bias from high-dimensional confounding, model misspecification, and unobserved operator heterogeneity. The results identify three distinct operational mechanisms. Higher passenger demand intensity increases incident rates by 0.38% (p<0.001). On the supply side, greater fleet supply adequacy and car-based operational intensity reduce incident rates by 0.52% (p<0.05) and 0.80% (p<0.01), respectively. Capacity utilization, which reflects the imbalance between demand and available supply, increases incident rates by 0.49% (p<0.001). These findings show that metro reliability depends not only on the level of demand or supply alone, but also on whether service provision keeps pace with passenger demand.
