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dc.contributor.authorChiou, Yu-Chiunen_US
dc.contributor.authorJou, Rong-Changen_US
dc.contributor.authorYang, Cheng-Hanen_US
dc.date.accessioned2015-12-02T02:59:17Z-
dc.date.available2015-12-02T02:59:17Z-
dc.date.issued2015-08-01en_US
dc.identifier.issn0965-8564en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.tra.2015.05.016en_US
dc.identifier.urihttp://hdl.handle.net/11536/127998-
dc.description.abstractAs the number of private vehicles grows worldwide, so does air pollution and traffic congestion, which typically constrain economic development. To achieve transportation sustainability and continued economic development, the dependency on private vehicles must be decreased by increasing public transportation usage. However, without knowing the key factors that affect public transportation usage, developing strategies that effectively improve public transportation usage is impossible. Therefore, this study respectively applies global and local regression models to identify the key factors of usage rates for 348 regions (township or districts) in Taiwan. The global regression model, the Tobit regression model (TRM), is used to estimate one set of parameters that are associated with explanatory variables and explain regional differences in usage rates, while the local regression model, geographically weighted regression (GWR), estimates parameters differently depending on spatial correlations among neighbouring regions. By referencing related studies, 32 potential explanatory variables in four categories, social-economic, land use, public transportation, and private transportation, are chosen. Model performance is compared in terms of mean absolute percentage error (MAPE) and spatial autocorrelation coefficient (Moran\' I). Estimation results show that the GWR model has better prediction accuracy and better accommodation of spatial autocorrelation. Seven variables are significantly tested, and most have parameters that differ across regions in Taiwan. Based on these findings, strategies are proposed that improve public transportation usage. (C) 2015 Elsevier Ltd. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectPublic transportation usage rateen_US
dc.subjectTobit regressionen_US
dc.subjectGeographically weighted regressionen_US
dc.subjectSpatial autocorrelationen_US
dc.titleFactors affecting public transportation usage rate: Geographically weighted regressionen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.tra.2015.05.016en_US
dc.identifier.journalTRANSPORTATION RESEARCH PART A-POLICY AND PRACTICEen_US
dc.citation.volume78en_US
dc.citation.spage161en_US
dc.citation.epage177en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000359169100013en_US
dc.citation.woscount0en_US
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