Analysis of passenger flow changes during urban rail transit emergencies based on big data

With the rapid development of urban rail transit systems in our country, the frequency of unexpected events has significantly increased. This poses a series of challenges for the networked operation and management of urban rail transit, especially the unpredictable and significant changes in passenger travel behavior during unexpected events. Therefore, studying the relevant issues of passenger flow distribution in urban rail transit during unexpected disruptions is of great theoretical and practical significance. In this paper, we analyze the passenger flow distribution based on the truthful AFC data of Beijing's rail transit system during a selected week in October 2017. We calculate the Origin-Destination (OD) passenger flow during working days and extract the OD passenger flow during the morning peak period in Beijing, which is used for the following two researches: (1) The normal situation passenger flow distribution. With the help of abundant historical data from the Automatic Fare Collection (AFC) system, the research establishes a passenger flow distribution model and algorithm based on the transformed OD matrix and the structural characteristics of the urban rail transit network, optimized through the MNL approach. This study serves as an experimental basis for passenger flow redistribution during emergency disruptions. (2) The interrupted passenger flow redistribution algorithm. Building on the basis of normal passenger flow distribution, the study analyzes and compares the passenger flow volume in disrupted segments and the transfer passenger flow before and after the interruption. By understanding the patterns of changes in passenger flow distribution before and after disruptions, it provides valuable guidance for post-disruption passenger evacuation.