Abstract
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.
Highlights
Urban rail transit (URT) has attracted more passengers as a main kind of transport mode because of its rapidity and punctuality
This paper studies the prediction of OD passenger flow in an emergency
The main conclusions are as follows: (1) Research on emergencies caused by signal faults and vehicle faults during the peak period is the focus of OD passenger flow prediction
Summary
Urban rail transit (URT) has attracted more passengers as a main kind of transport mode because of its rapidity and punctuality. C. Li et al.: Spatial-Temporal Correlation Prediction Modeling of OD Passenger Flow Under URT Emergency Conditions a decline in the transport capacity or even paralysis of the URT network. URT system equipment faults during daily operation are the most common events, and the proportions of signal failure and vehicle failure were 59.21% and 26.86%, respectively, for BURT emergencies in one year, which were the main types of equipment failures. The analysis subject in this paper is OD passenger flow under unpredictable emergencies, which results from signal and vehicle equipment failures during rush hour. The purpose of this study is to predict changes in OD passenger flow by exploring the spatial-temporal distribution rules of passenger flow after a URT emergency, which provides effective support for passenger flow evacuation. Based on 35-day AFC data in the BURT system, OD passenger flow prediction in cases of emergency is studied.
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