{"title":"How is Intraday Metro Ridership related to Station Centrality in Athens, Greece?","authors":"Athanasios Kopsidas, K. Kepaptsoglou","doi":"10.32866/001c.75171","DOIUrl":null,"url":null,"abstract":"In this study, intraday correlations between station centralities and ridership at stations of the Athens metro system in Greece are explored. An unweighted L-space representation of the physical metro network is developed, and degree, closeness and betweenness are selected as station centrality measures. Hourly smart-card data are used for representing passenger flows. For station classification, principal component analysis and k-means clustering are utilized. The findings suggest that centrality and ridership usually move in opposite directions, morning peak-hour boardings are completely uncorrelated with station centrality, and metro stations can be classified as ‘central destinations’, ‘averagely central origins’, and ‘underutilized peripheral stations’.","PeriodicalId":73025,"journal":{"name":"Findings (Sydney (N.S.W.)","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Findings (Sydney (N.S.W.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32866/001c.75171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, intraday correlations between station centralities and ridership at stations of the Athens metro system in Greece are explored. An unweighted L-space representation of the physical metro network is developed, and degree, closeness and betweenness are selected as station centrality measures. Hourly smart-card data are used for representing passenger flows. For station classification, principal component analysis and k-means clustering are utilized. The findings suggest that centrality and ridership usually move in opposite directions, morning peak-hour boardings are completely uncorrelated with station centrality, and metro stations can be classified as ‘central destinations’, ‘averagely central origins’, and ‘underutilized peripheral stations’.