新冠病毒肺炎疫情在过饱和城市轨道交通系统中传播风险较高，为降低交叉感染风险，同时缓解车站日益严重的拥挤状况，本文提出了一种面向城市轨道交通客流控制与列车时刻表一体化的分布鲁棒优化方法。具体地，考虑客流需求和随机客流场景发生概率的双重不确定性，以最小化列车运营时间、平均等待人数和运营风险为目标，构建了基于'均值-CVaR'准则的分布鲁棒优化模型，并推导了其与传统两阶段随机规划模型之间的关系。基于∞-范数非精确集，将该模型等价转化为计算可处理形式，并设计了一种结合局部搜索规则和CPLEX的求解算法。最后，以实际运营数据为背景，通过一系列数值算例验证了所提方法的有效性。

Regular coronavirus disease 2019 (COVID-19) epidemic prevention and control have raised new requirements that necessitate operation-strategy innovation in urban rail transit. To alleviate increasingly serious congestion and further reduce the risk of cross-infection, a novel two-stage distributionally robust optimization (DRO) model is explicitly constructed, in which the probability distribution of stochastic scenarios is only partially known in advance. In the proposed model, the mean-conditional value-atrisk (mean-CVaR) criterion is employed to obtain a tradeoff between the expected number of waiting passengers and the risk of congestion on an urban rail transit line. The relationship between the proposed distributionally robust model and the traditional two-stage stochastic programming (SP) model is also depicted. Furthermore, to overcome the obstacle of model solvability resulting from imprecise probability distributions, a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form. A hybrid algorithm that combines a local search algorithm with a mixedinteger linear programming (MILP) solver is developed to improve the computational efficiency of largescale instances. Finally, a series of numerical examples with real-world operation data are executed to validate the proposed approaches.