Prediction of bike-sharing trip counts: comparing parametric spatial regression models to a geographically weighted XGBoost algorithm

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dc.contributor.authorSchimohr, Katja
dc.contributor.authorDoebler, Philipp
dc.contributor.authorScheiner, Joachim
dc.date.accessioned2023-12-19T07:59:52Z
dc.date.available2023-12-19T07:59:52Z
dc.date.issued2022-11-29
dc.description.abstractRegression models are commonly applied in the analysis of transportation data. This research aims at broadening the range of methods used for this task by modeling the spatial distribution of bike-sharing trips in Cologne, Germany, applying both parametric regression models and a modified machine learning approach while incorporating measures to account for spatial autocorrelation. Independent variables included in the models consist of land use types, elements of the transport system and sociodemographic characteristics. Out of several regression models with different underlying distributions, a Tweedie generalized additive model is chosen by its values for AIC, RMSE, and sMAPE to be compared to an XGBoost model. To consider spatial relationships, spatial splines are included in the Tweedie model, while the estimations of the XGBoost model are modified using a geographically weighted regression. Both methods entail certain advantages: while XGBoost leads to far better values regarding RMSE and sMAPE and therefore to a better model fit, the Tweedie model allows an easier interpretation of the influence of the independent variables including spatial effects.en
dc.identifier.urihttp://hdl.handle.net/2003/42238
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24073
dc.language.isoende
dc.relation.ispartofseriesGeographical analysis;55(4)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subject.ddc710
dc.titlePrediction of bike-sharing trip counts: comparing parametric spatial regression models to a geographically weighted XGBoost algorithmen
dc.typeTextde
dc.type.publicationtypeArticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationSchimohr, K., Doebler, P. and Scheiner, J. (2023), Prediction of Bike-sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm. Geographical Analysis, 55: 651-684. https://doi.org/10.1111/gean.12354de
eldorado.secondarypublication.primaryidentifierDOI https://doi.org/10.1111/gean.12354de

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