基于云模型-Markov链的城市轨道交通车站客流状态分析

在大客流条件下，城市轨道交通车站为缓解大客流的冲击，需要全面地分析和准确地预测车站客流状态，进而采取大客流组织措施.本文在明确车站结构设施设备布局的基础上，针对车站不同的设施设备进行了客流状态划分，确定了车站设施设备客流状态等级，基于此，构建了车站客流状态辨识模型，通过状态隶属关系结合马尔科夫状态转移理论分析了客流状态的动态变化情况，系统地实现了设施设备客流状态级别的判断与动态预测.通过实例分析，验证了该方法能够较准确地判别车站客流状态变化情况，为车站安全运营管理、客流控制、应急处置等方面提供决策支持，具有较强的应用价值.

Research on Passenger Flow State in Urban Rail Transit Station Based on Cloud Model and Markov Chain

In the condition of large passenger flow, station managers of urban rail transit take passenger flow organization measures to relieve passenger flow pressure. It is necessary for passenger flow state of different facilities in urban rail transit station to be comprehensively analyzed and accurately forecasted. In this paper, passenger flow state levels of different facilities in urban rail transit station were divided, and passenger flow state identification model was proposed based on passenger flow state level. Dynamic change of passenger flow state was analyzed according to state affiliation and Markov Chain, and the passenger flow state level identification and dynamic prediction was realized. Finally, a case was used to verify the feasibility of the method. Station passenger flow state can be accurately identified by this method, which provides decision support for station security operation management, passenger flow control, emergency handling and so on.