## 4 Concluding Remarks  
This paper proposed a method to identify abnormal stations and evaluate the intensity of passenger flow at abnormal stations affected by disruption. Then, the propagation law of the disruption in the urban rail transit network was analyzed based on large amounts of AFC data. Taking the Beijing rail transit system as a case study, all abnormal stations were dynamically identified, and the influence intensity of passenger flow at abnormal stations was also dynamically calculated. To enhance the readability, the spatiotemporal distribution characteristics of abnormal stations were analyzed at the macro level. At the micro level, the OD passenger flow at different types of representative stations with greater influence intensity after the disruption was analyzed in detail. A summary of the analysis of the influence and propagation law of urban rail transit disruptions is presented as follows:  
1. The proposed method relies on only real-time AFC data, making it simple and easy to implement in the daily operation of any urban rail transit system. It is suitable for the real-time monitoring and dynamic identification of the daily operational risks caused by disruptions in the context of network operation. Accordingly, it can effectively improve the intelligent management of urban rail transit systems. 2. By analyzing the spatiotemporal distribution of abnormal stations in the network after the disruption, it was found that the first stations to be affected were those adjacent to the disrupted station. The tap-in passenger flow at these stations decreased significantly at the beginning of the disruption. Generally, the disruption did not affect ordinary stations on other lines. Moreover, all stations affected by the disruption could be sorted by their recovery speed, from fastest to slowest, as follows: ordinary stations that were far away from the disrupted station, adjacent stations, and transfer stations. The duration for which transfer stations were affected by the disruption was the longest. Furthermore, in contrast to transfer stations, ordinary stations affected by the disruption did not suffer an increased tap-in passenger flow after the disruption. 3. At the disrupted station, some passengers chose to wait outside the station during the disruption and to continue their travel once the disruption was resolved. However, at ordinary stations far away from the disrupted station, passengers left the urban rail transit system (i.e., chose other travel modes, abandoned travel, etc.) rather than waiting outside the station during the disruption. Furthermore, at the transfer stations affected by the disruption, many travelers chose to change their travel routes from origins to destinations. 4. Compared with the long-distance travel demand, the short-distance travel demand was more affected by the disruption because it was more convenient for passengers to choose ground-level transportation as an alternative to continue their travel after the disruption. However, long-distance ground-level transportation is not as efficient as urban rail transit in terms of travel time, which led to more long-distance passengers choosing to wait outside the station and continue their travel after the disruption was resolved. However, this study had some limitations. The first limitation of our study was that the AFC data for the urban rail transit system were old. This was because the AFC data for urban rail transit are managed by government departments and operational agencies and are not open to the public. The report information on urban rail transit disruptions, such as when the disruption occurred, where it occurred, and how long it lasted, is also contained in confidential operational documents. The second limitation of our study was that we only analyzed a disruption caused by a signal interruption. We were unable to analyze other types of emergencies, such as equipment failure, natural disasters, and terrorist attacks.