Abstract
Large-scale passenger flows occur frequently during the peak hours of urban rail transit stations and on holidays. Thus, the timely and accurate early warning of impending large-scale passenger flows can positively impact the operational safety of the entire station. By further deepening the definition of passenger flow warnings in stations, a new model of urban rail transit station passenger flow based on system dynamics is constructed. The method of determining the key area of passenger flows in the early warning stage based on streamlines is proposed; the key indicators and thresholds affecting early warnings are studied. Finally, taking a typical station as an example, a station model is built using Anylogic software. The parameter sensitivity analysis is used to determine the impact of each key indicator on the passenger flow in the key area of the station early warning, and the reference threshold of each indicator is determined.
Highlights
Due to the special geographical locations and building structures of urban rail transit stations, passengers' personal safety and station operation safety can be compromised if very large passenger flows occur without warning
1) According to the real experience of station operations, we have further enhanced the definition of the station early warning and established the station passenger flow model based on system dynamics
2) We have proposed a specific method for determining the key areas of the station early warning based on streamlines as well as the threshold values of key indicators
Summary
Due to the special geographical locations and building structures of urban rail transit stations, passengers' personal safety and station operation safety can be compromised if very large passenger flows occur without warning. In such situations, station management personnel may be unable to issue warnings and take passenger flow control measures in time. Zhao et al [2,3] studied early warning classification methods for passenger flow in the stations. The station early warning methods and key indicator thresholds are studied to provide a rigorous foundation for the research
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