## 1 Introduction
Several simulation methods have been used to simulate different emergency scenarios and measure the influence of disruption on urban rail transit systems [23; 24; 25; 26]. For example, [PERSON] et al. [23] analyzed the influence of disruption on passenger flow in an urban rail transit network by a simulation method. When the urban rail transit system was interrupted for a long time, a large number of passengers were stranded at stations. [PERSON] et al. [27] used historical passenger flow data to estimate the stranded passenger flow at abnormal stations. The third large stream of literature has paid attention to the evacuation of passengers after emergencies [28; 29]. [PERSON] and [PERSON] [28] studied the emergency evacuation of urban rail transit systems during fire scenarios. [PERSON] et al. [29] took the Beijing transit network as an example to simulate emergency evacuation at an urban metro station. Other scholars have aimed to investigate emergency response and bus bridging services [30; 31; 32; 33]. [PERSON] et al. [30] investigated how to recover from disruptions in an urban rail transit network. [PERSON] and [PERSON] [31] studied recovery from disruptions in rapid transit networks. In response to rail transit system disruptions, bus bridging services can evacuate stranded passengers and mitigate the adverse influence of disruptions. [PERSON] et al. [32] developed a path-based multi-commodity flow formulation for bus bridging service design. However, the recovery time for disruptions may be uncertain. [PERSON] et al. [33] provided an alternative scheme to evacuate passengers via the urban bus system considering an uncertain disruption recovery time and heterogeneous risk-taking behavior. Previous studies have made great contributions to research on the impact of emergencies on urban rail transit. Most studies have paid attention to passenger behavior after emergencies and the evacuation of passengers. A few studies have focused on the influence of emergencies on passenger flow. Calculating the real-time influence intensity of passenger flow at each station and analyzing the propagation law of disruptions in urban rail transit networks are prerequisites for urban rail transit managers to determine rational strategies. Due to the difficulty in obtaining real-world data on urban rail transit and the complexity of passengers' choice behavior, most studies have used simulation methods to analyze the influence of disruptions on passenger flow. However, since simulation methods are affected by parameter calibration and assumptions, the results of the propagation law of urban rail transit disruptions may be inconsistent with real-world situations. This approach is not suitable for real-time dynamic passenger flow monitoring when disruptions occur. Based on previous studies, after assuming that the passenger flow at each station in a normal situation meets normal distribution, we proposed a method for calculating the influence intensity of passenger flow at abnormal stations during disruptions and analyzed the spatiotemporal distribution of the influence characteristics. Our contributions herein are summarized below. First, based on the large amounts of AFC data for Beijing rail transit, we calculated and displayed the fluctuation threshold of the tap-in passenger flow during the normal operation of the urban rail transit system. Accordingly, a practical method to identify abnormal stations affected by disruptions was proposed. Second, this paper presents a real-time calculation method for the intensity of passenger flow affected by disruptions at abnormal stations. The influence intensity of the tap-in passenger flow at abnormal stations was calculated and displayed in real time. Since the proposed method relies on only real-time AFC data, it is simple and easy to apply in the daily operation of any urban rail transit system. Moreover, it is suitable for the real-time monitoring and dynamic identification of daily operational risks. It can effectively improve the intelligent management of urban rail transit systems.