Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm

Katja Schimohr · Philipp Doebler · Joachim Scheiner

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Urban Transport and Accessibility Spatial and Panel Data Analysis Human Mobility and Location-Based Analysis

Abstract

Regression 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.

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References

1970A Computer Movie Simulating Urban Growth in the Detroit Region 2007Methods to account for spatial autocorrelation in the analysis of species distributional data: a review 1998Geographically Weighted Regression 2019Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction 2014The impact of weather conditions on bikeshare trips in Washington, DC 2015Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations 2021Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic 2020Using data mining techniques for bike sharing demand prediction in metropolitan city

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2025Geographical-XGBoost: a new ensemble model for spatially local regression based on gradient-boosted trees 2025The Fundamental Issues and Development Trends of AI-Driven Transformations in Urban Transit and Urban Space 2025Shared e-bikes demand in urban mobility: Temporal heterogeneity, driving factors, and strategic implications 2025Differences in bike-sharing usage and its associations with station-surrounding characteristics: A multi-group analysis using machine learning techniques

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