Spatial-Temporal Correlation Prediction Modeling of Origin-Destination Passenger Flow Under Urban Rail Transit Emergency Conditions

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 spatial-temporal 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.