Simulation-based Optimization Of Carrying Capacity Of Urban Rail Platform Under Cross-platform Transfer

One-platform interchange is a convenient transfer organization pattern in urban rail transit network operating system. The trains stopping either side of the platform could belong to different lines, or different grade in the same line. One-platform interchange system can be the seamless connection, or we say "easy transfer", which means the minmise of the walking distance and waiting time, with the transfer time cohesion scheme under networked operation. This interchange system is a service platform for passenger flow interaction between the train system and the station system, and there are three features of the passenger flow distribution in it. First, the flow direction of the passengers on platform is complex. Then, the passenger flow distribution on platform and the passengers’riding route selection is stochastic. Finally, it is easy to crowd on the platform when both side of the platform arrive the train, the collection and distribution capacity of the platform will be saturated so the effiency of interchange system get reduced.Although there are many researches of the urban rail network operation, the study on the dynamic variation rule of passenger carrying capacity of urban rail platform system is less. Especially few research focus on the interchange effiency and safety of the One-platform interchange system. On that account, this paper will aim to the key issues of the One-platform interchange system. The paper investigates how to predict the temporary crowded of the system, evaluates the utilization of the platform passenger carrying capacity, and puts forward pointed scheme to strengthen the interchange effiency. The research achievement could be practical and useful.The paper research work can be summarized as follows:(1) Learn the definition of the carrying capacity in the structural mechanics, ecology, environics, etc. Come up with the definition of passenger carrying capacity of the platform and the computing method, according to the structure, of physical station system, the dynamic conditions under the Network train operation organization, and the random conditions under the passengers’ riding route selection. Give the influence mechanism of passenger carrying capacity with the physical infrastructure, network operation scheme, and the passengers’ selection.(2) Accurate prediction of passengers’ route selection has a great influence to the occurrence dynamics and temporarily saturated of the platform passenger carrying capacity, where the prediction is also the basic of the carrying capacity simulation. So this paper aims to solve the mult-route passenger flow assignment problem, establishes the Rail transit Riding route selection Multinomial logit model, which based on the random utility theory. The model could distribute the dynamic passenger flow in the One-platform interchange system, by using the RP&SP survey data. The result summarizes the different preferences of travel demand between commuters and relaxers. The different preferences of travel demand include acceptance level of the interchange waiting time, the congestion in the car, etc. The result shows that different kind of passengers get different demand to the time saving, the interchange less, and the less congestion.(3) The core research content of this paper is to establish the simulation model based on the colored stochastic Petri nets, based on the platform passenger carrying capacity computing method and the Rail transit passenger riding route selection Multinomial logit model. The simulation model not merely simulating the platform train flow and passenger flow, but also possess evaluation function about the efficiency of platform passenger flow distribution. The administrator could grasp the saturated moment of the passenger flow carrying capacity of platform, according to monitor the changes of distribution efficiency indicators. So this is suitable for rail transit daily operation safety management. The case studies shows that under the same interchange passenger flow demand condition, the time and platform area are different, even if ues the same network operation scheme.(4) Finally, think about the "incomplete information" and "indetermin information" of the platform, the paper established the Grey programming model to calculate the utilization rate of the platform passenger carrying capacity each transfer cycle. The model would give the optimal decision of strengthening scheme according to the platform distribution efficiency index. The case study shows that the Grey programming model can take the reason of carrying capacity saturation into account and choose appropriate redeployment to improve the passenger flow distribution efficiency, which could guarantee the safe and efficient of the platform system.