# Subway Station Capacity Maintained by Optimizing a Maintenance Schedule of Key Equipment
## 1 Introduction  
Subway stations play an important role in urban rail transit networks since passengers gather for boarding or alight at these locations. The service capacity of each station has a direct impact on the capacity of subway line and also on the whole subway network. Therefore, it is critically important to keep the station service capacity above an acceptable level in its full life cycle. The station equipment's performance deteriorates and its reliability decreases over time. When station equipment fails, station capacity declines and the travel time of passengers increases, especially during rush hour periods. Therefore, how to optimize key equipment maintenance schedule to maintain the station capacity in the full life cycle is significantly important for subway operation companies. The subway station capacity (SSC) issue has received some attention in the last two decades (see references [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]). There are a few definitions involving SSC in the available literature. In references [1; 2], SSC is defined as the maximum passenger flow through various facilities, such as stairs, escalators and gates, at one subway station. However, the influence of passenger flow is not considered in this definition. To solve the problem considered in this paper, dynamic SSC is defined later according to the gathering and scattering process of passengers. There have been a few methods to calculate or evaluate SSC in the available literature. The methods are classified into two main categories according to the modeling approach: the mathematical model-based method and the simulation-based method. The first approach evaluates the subway station system by means of mathematical formulas. The M/G/C/C state dependent queuing network is used in mathematical models of subway stations, especially for the staircase and the pavement (see references [3; 4; 5; 6]). In reference [7], an M/G/C/C-based capacity model of staircases and corridors is proposed for passenger evacuation in subway stations through analysis of passenger movements. However, thereis little research on the evaluation or that calculates SSC in a holistic fashion. In references [8; 9], an analytical queuing network model for a subway station including two subsystems is built, of which the first subsystem is built by the M/G/C/C state dependent queuing network and the second is constructed by the probability theory. However, equipment failure is not considered in the station capacity model which affects SSC. Simulation models (see references [10; 11; 12; 13; 14; 15]) are suitable for animating stations in different perspectives, but only a few researchers have considered the capacity calculation problem. For example, a simulation model based on hybrid Petri nets is presented in [11]. A modeling approach based on discrete-event simulation and response surface methodology (RSM) deals with the optimization of average passenger travel time inherent in the metro planning process in reference [12]. In the references [13; 14], the station simulation model based on AnyLogic is established to simulate the gathering and scattering process and evaluate the usage of device. In reference [15], an AnyLogic model is established to analyze the current status of the subway station. These studies focused on how to model the station, evaluate the usage of device or identify bottlenecks of a station. However, a method to calculate the station capacity integrating simulation models has not been reported yet. The passenger carrying capacity of a rail transit station is calculated by simulation model based on the system dynamics theory [2]. Similarly, the software AnyLogic based on social force model can be used to calculate SSC. The reliability analysis of the key equipment such as the gate and the escalator play a critical role in our study. However, little research on reliability analysis of key equipment at subway stations has been conducted, to the best of our knowledge.