作为城市系统中大规模人群集聚的重要载体，地铁系统面临着日益增长的突发事件应急管理压力。在相对封闭的地铁站台内部，人群的心理和行为极易受到突发事件的扰动，从而引发不可预期的灾难性事件。本项目将针对地铁网络的典型非常规突发事件，利用大数据分析技术和复杂网络理论，研究地铁网络中大规模人群流动的时空复杂性；综合分析地铁车流网和人群流动网络之间的动态耦合与演化过程，建立基于城市地铁的车流-人流动态耦合网络；研究非常态情景下的异常扰动在地铁网络中的传播规律，并建立地铁网络韧性评价模型；建立典型的非常态情景，研究地铁系统中突发事件的产生和演化机制；应用业务连续性理论，对地铁客流业务进行突发事件冲击分析，制定地铁客运业务连续性策略。研究成果可为地铁网络系统应对异常客流冲击提供科学的理论指导，从而为地铁系统业务可持续性管理提供支持。

As an important carrier for large-scale population in urban system, the metro system is faced with increasing pressure of emergency management. Inside the relatively closed space of subway station, the mind and behavior of crowds can be easily influenced by emergency disturbance, which may cause unpredictable catastrophic events. This project will focus on the typical unconventional emergencies in urban subway, and use big data and complex network theory to study the spatiotemporal complexity of large-scale populations. By analyzing the dynamic coupled evolution of both traffic and passenger flow, the traffic-passenger dynamic coupled network will be established. Based on the abnormal disturbance propagation law under emergency, the resilience evaluation for metro system will be proposed. Through the simulation of typical abnormal situation, the generation and evolution mechanism of subway emergency will be studied. With the application of business continuity management, the emergency impact analysis of subway operation will be carried out and the business continuity strategy will be generated. This study will provide scientific guidance for emergency response under abnormal traffic, and provide support for sustainability management in metro system.