Exploring the association between network centralities and passenger flows in metro systems

Applied Network Science

Research

Aristeides Douvaras¹  Konstantinos Kepaptsoglou²  Athanasios Kopsidas² 

[1] Athens University of Economics and Business, 47A Evelpidon Str. & 33 Lefkados Str., Athens, Greece;School of Rural and Surveying Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, Zografou Campus, 15773, Athens, Greece;

关键词: Network theory;    Centrality;    Metro system;    Passenger flows;    Linear regression;    Machine learning;    XGBoost;   

DOI  :  10.1007/s41109-023-00583-2

 received in 2023-02-24, accepted in 2023-08-16,  发布年份 2023

来源: Springer

【 摘 要 】

Network science offers valuable tools for planning and managing public transportation systems, with measures such as network centralities proposed as complementary predictors of ridership. This paper explores the relationship between different cases of passenger flows at metro stations and network centralities within both metro and alternative public transport (substitute) networks; such an association can be useful for managing metro system operations when disruptions occur. For that purpose, linear regression and non-parametric machine learning models are developed and compared. The Athens metro system is used as a testbed for developing the proposed methodology. The findings of this study can be used for deriving medium-term ridership estimates in cases of metro disruptions, as the proposed methodology can support contingency plans for both platform and rail track disruptions.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

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