Calculation Of Station Service Capacity And Adaptability In Urban Rail Transit

Station design and operation management of rail transit is one of the most important subjects in construction design, traffic engineering, public safety management and soon. Nowadays, passenger flow has surged in rail transit stations and passenger flow limiting has been a common issue to consider. It is necessary to research on how to describe the collection and distribution process of passengers in stations, how to systematically analyze and calculate subway station capacity in given service levels and how to quantitatively evaluate adaptability of station capacity in uncertain demand and organize passenger flow considering multi-station collaboration. It is the crucial basis of station design, station passenger organization, facility usage plan and train operation organization. The study object of this paper is rail transit stations. And it considers passenger collection and distribution characters and focuses on station service capacity calculation, usage and enhancement. The main contents include station service capacity definition and system, station service capacity calculation, adaptability evaluation of station service capacity in uncertain demand and passenger flow organization considering multi-station collaboration. Then a typical station is chosen as a real example.This paper has finished the following work.(1) A concept system of rail transit station service capacity is established.Based on the existing concepts and definitions of transportation capacity, we consider the systematic operation flow in stations and propose the station service capacity concept which reflects different passenger crowding degrees and safety characters (i.e. service levels). And we form the station capacity theory with different hours and different types of passengers, which considers the demands of both operators and passengers. Then we analyze uncertainty characters (including change of total number of passengers and passenger flow space distribution) in daily short time and medium and long time in the future. Change of station service capacity in the conditions of different passenger flow volumes and space distributions is also analyzed to propose three kinds of station service capacities. They are maximum station service capacity, standby station service capacity and station service capacity with single changeable demand. Then we explain usage scenarios for each capacity and its physical meaning. Next we analyze impact factors of station service capacity, how to express the problem of station service capacity in mathematical forms and how to structure mathematical models for the three kinds of station service capacity.(2) We build a layered queuing network model of station collection and distribution process and a queuing network optimization model of station service capacity. Then we propose an analytical method of station service capacity and use a real example of Beijing Station in Beijing Subway to verify validity of the models and algorithms. After that, we analyze sensitivity of station service capacity with single changeable demand.We analyze passenger flow characters of single facility (stairs, passages, gates, platforms and soon) in rail transit station and build a crowding state dependent queuing model of single facility (node) and a Markov queuing model of number of passengers left behind in the platform. After building these two models, a layered station network analytical model based on M/G/C/C state dependent queuing is formed. Then we build a mathematically analytical model of station service capacity with multi-objective and non-linear constraints and use RSM to solve this model. Finally, we use data of Beijing Station in Beijing Subway to calculate its station service capacity with single changeable demand in a rush hour and verify its feasibility and validity. Then sensibility is used to conclude the bottleneck facility of station service capacity with single changeable demand.(3) Technologies of how to build a queuing network simulation model and how to simulate station service capacity are developed. A simulation and solving method of station service capacity calculation is proposed and a kind of simulation analysis technology is formed. A real case of Beijing Station in Beijing Subway is chosen to compare and analyze efficiency and adaptability ranges of analytical method and simulation method.This paper researches on the simulation and modeling technology which combines node dynamic choice behavior of single passenger and macroscopic passenger flow collection and distribution. And it creates the station passenger collection and distribution simulation platform which combines queuing network and node choice behavior characters of single passenger. As to the characteristics of complexity in simulation optimization model solving and large scale of decision variables, we use GA to design the optimal simulation plans. Then DEA is chosen to evaluate results (i.e. adaptability degree) of different test plans, which can overcome the shortcoming of difficulty in setting weight factors of multi-objective in the adaptability degree function. Thus we get the simulation analytical method of station service capacity which combines DEA and GA. Finally, data of Beijing Station and Xi’erqi Station in Beijing Subway is used to verify feasibility and validity of the simulation analytical model. After that, we compare the differences in efficiency and application ranges of mathematically analytical method and simulation method.(4) As to the problem of wide fluctuation in total demand of station passenger flow and big change in passenger flow characters, we analyze the relations among space distribution of passenger flow demand, quantity of demand and station service capacity. Then we propose the definition and measure method of station service capacity adaptability.We divide capacity adaptability into two categories, one with unchangeable space d istribution and one with changeable space distribution. When space distribution is uncha ngeable, we use sta’ndby service capacity to measure capacity adaptability. When space distribution is changeable, we propose two kinds of evaluation methods, overall adaptab ility and adaptability with single changeable passenger flow demand, to measure capacit y adaptability. Overall adaptability is used to evaluate the maximum demand that can be served in the station in the condition of changeable passenger flow demands (total quan tity of demand and demand space distribution), which can be measured by maximum se rvice capacity. Adaptability with single changeable passenger flow demand (single dem and adaptability) is used to evaluate the maximum passenger flow demand that can be s erved in the station when single passenger flow demand changes, which can be calculate d by service capacity with single changeable demand. Finally, we use the examples of B eijing Station in Beijing Subway (a general station) and Xi’erqi Station in Beijing Subw ay (a transfer station) to verify validity of capacity adaptability evaluation method. In th ese examples, we focus on the single demand adaptability of station service capacity alo ng with change of inflow demand and transfer demand in Xi’erqi Station and make stati on passenger flow limiting plans in different scenarios.(5) Considering the fact that the demand is far beyond the capacity offered by stations and lines in Beijing Subway in the rush hour in the morning and evening, we study how the passenger flow in single station propagates in the following stations, build a multi-station collaboration passenger flow limitingmodel on the basis of single station capacity analysis and evaluation and give the relevant solving algorithm Then we use the example of some sections in Beijing Subway Line13to analyze and verify validity of the model and algorithm. We analyze passenger flow crowding propagation law of adjacent stations in theory and evaluate effects of different passenger flow limitingstrategies. Our work can offer a theoretical and technological support to multi-station collaboration passenger flow limitingresearch.