Establishing a passenger flow prediction mechanism is necessary for quickly evacuating many passengers in an emergency, which can improve the service quality of urban rail transit (URT). To effectively forecast origin-destination (OD) passenger flows in URT under emergency conditions, 35-day automatic fare collection (AFC) data are used for a statistical analysis of the time, location and passenger flow aspects. The influence range of the OD passenger flow during an emergency is determined by analyzing the degree of passenger flow fluctuation. Considering the time period of an emergency occurrence and its continuous influence, this paper also studies the influence of an emergency occurring at a station, a section between two stations or a section across several stations. A spatial-temporal correlation prediction model of OD passenger flow based on nonlinear regression is constructed by introducing the concept of passenger flow spatialtemporal influencing parameters. According to the characteristics of URT lines, a passenger flow prediction algorithm is proposed to predict the OD passenger flow for different line categories for an emergency. A real typical emergency involving the Beijing urban rail transit (BURT) system in 2017 is analyzed to verify the effectiveness of the proposed model. The results show that this model can effectively predict OD passenger flow in a URT system during an emergency, which provides basic support for the evacuation of passengers. INDEX TERMS Urban rail transit (URT) emergency, origin-destination (OD) passenger flow prediction, spatial-temporal correlation, nonlinear regression, automatic fare collection (AFC) data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.