 A multiscale spatial analysis of taxi ridershipTao Lyu, Yuanqing Wang, Shujuan Ji, Tao Feng and Zhouhao Wu Journal of Transport Geography, 2023, vol. 113, issue C Abstract: Taxi plays a supplement role in sustainable development of urban public transport systems. However, the extent to which the built environment affects taxi ridership at various spatial scales deserves further exploration because understanding the true spatial heterogeneity across a varying scale could be valuable for both global and localized policy decision-makings. In this study, we attempt to analyze and discuss the spatial predictors of taxi ridership by utilizing an multiscale geographically weighted regression (MGWR) model and comparing the model's performance to that of ordinary least square (OLS) and geographically weighted regression (GWR) models. Using the taxi data of Xi'an city, we found that the MGWR model could explain 81.8% of the total taxi ridership fluctuations and allows localized and targeted policy makings to help taxi drivers search for passengers and to improve passengers' taxi-hailing experiences in specific districts. Keywords: Multiscale geographically weighted regression; Influencing factors; Taxi ridership; Spatial heterogeneity; Scale effect (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jotrge:v:113:y:2023:i:c:s0966692323001904 DOI: 10.1016/j.jtrangeo.2023.103718 Access Statistics for this article Journal of Transport Geography is currently edited by Frank Witlox More articles in Journal of Transport Geography from Elsevier |
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