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.