{"title":"Exploring the Utility of Gini Coefficients as a Measure of Temporal Variation in Public Transit Travel Time","authors":"J. Chestnut, E. Boschmann","doi":"10.1080/23754931.2021.1964582","DOIUrl":null,"url":null,"abstract":"Abstract Travel times by public transit vary dramatically throughout the day. However, these variations are not the same for all commutes. If one commute origin and destination are connected by high frequency transit all day, then that commute will have less variation in travel time throughout the day. This research illustrates how that variation can be measured with gini coefficients so that inequality in travel times can be better understood. Using Denver as a case study, gini coefficients are calculated with 120 travel times by public transit throughout the day for each of over 200,000 origin destination pairs. The utility of the gini coefficient as a predictor of the percent of the population that drives to work is then explored using a regression. The regression results indicate that the gini variable has utility in predicting the percent of the population that drives to work.","PeriodicalId":36897,"journal":{"name":"Papers in Applied Geography","volume":"6 1","pages":"112 - 123"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Papers in Applied Geography","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23754931.2021.1964582","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 1
Abstract
Abstract Travel times by public transit vary dramatically throughout the day. However, these variations are not the same for all commutes. If one commute origin and destination are connected by high frequency transit all day, then that commute will have less variation in travel time throughout the day. This research illustrates how that variation can be measured with gini coefficients so that inequality in travel times can be better understood. Using Denver as a case study, gini coefficients are calculated with 120 travel times by public transit throughout the day for each of over 200,000 origin destination pairs. The utility of the gini coefficient as a predictor of the percent of the population that drives to work is then explored using a regression. The regression results indicate that the gini variable has utility in predicting the percent of the population that drives to work.