Paper
6 February 2022 Empirical study on the stability of passenger flows in the Beijing rail transit
Xing Qiu, Peng Zhao
Author Affiliations +
Proceedings Volume 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021); 120812L (2022) https://doi.org/10.1117/12.2623881
Event: Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 2021, Chongqing, China
Abstract
Urban rail transit has prominent characteristics of the main passenger flow [1], and commuters occupy the major part of the flow. Commuters have obvious regularity in travel time and travel space, which makes passenger flow show obvious spacetime stability. However, there are certain prerequisites and boundaries for the stability of passenger flow. Only by clarifying the premise, can historical passenger flow be used as a reference more effectively. There is a lack of quantitative research on the stability of passenger flow under different time granularities in previous studies. To this end, this article used the Beijing subway AFC data to construct a passenger flow time series to analyze and measure the stability of passenger flow under different passenger flow objects and different time granularities, and find the time granularity value required for different passenger flows to achieve strong stability, finally divide the stability level of the station. The analysis results show that the stability of OD passenger flow is generally lower than that of inbound passenger flow, and the stability of passenger flow in different time periods is morning peak on working days< evening peak on working days< weekends < flat peak on working days.
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Xing Qiu and Peng Zhao "Empirical study on the stability of passenger flows in the Beijing rail transit", Proc. SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 120812L (6 February 2022); https://doi.org/10.1117/12.2623881
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KEYWORDS
Analytical research

Data modeling

Mining

Artificial neural networks

Astatine

Cognition

Data mining

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