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

IF 1.3 Q3 COMPUTER SCIENCE, THEORY & METHODS Applied Network Science Pub Date : 2023-09-22 DOI:10.1007/s41109-023-00583-2
Athanasios Kopsidas, Aristeides Douvaras, Konstantinos Kepaptsoglou
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引用次数: 1

Abstract

Abstract 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.
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探索地铁系统中网络中心性与客流之间的关系
网络科学为规划和管理公共交通系统提供了有价值的工具,并提出了网络中心度等措施,作为客流量的补充预测指标。本文探讨了不同情况下地铁站客流与地铁和替代公共交通网络中网络中心度之间的关系;这种关联对于在发生中断时管理地铁系统的运行非常有用。为此,开发并比较了线性回归和非参数机器学习模型。雅典地铁系统被用作开发所提出方法的试验台。这项研究的结果可用于在地铁中断的情况下得出中期乘客估计,因为所建议的方法可以支持月台和轨道中断的应急计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Network Science
Applied Network Science Multidisciplinary-Multidisciplinary
CiteScore
4.60
自引率
4.50%
发文量
74
审稿时长
5 weeks
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