Prediction of Bike‐sharing Trip Counts: Comparing Parametric Spatial Regression Models to a Geographically Weighted XGBoost Algorithm
Author:
Affiliation:
1. Department of Spatial Planning, Transport Research Group Technische Universität Dortmund Dortmund Germany
2. Department of Statistics, Research Group of Statistical Methods in Social Sciences Technische Universität Dortmund Dortmund Germany
Publisher
Wiley
Subject
Earth-Surface Processes,Geography, Planning and Development
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/gean.12354
Reference69 articles.
1. Forecasting the Carsharing Service Demand Using Uni and Multivariable Models;Alencar V. A.;Journal of Internet Services and Applications,2021
2. Dockless E‐Scooter Usage Patterns and Urban Built Environments: A Comparison Study of Austin, TX, and Minneapolis, MN;Bai S.;Travel Behaviour and Society,2020
3. Spatial Analysis of Bikeshare Ridership With Smart Card and POI Data Using Geographically Weighted Regression Method;Bao J.;IEEE Access,2018
4. Bivand R.andD.Yu(2020).spgwr: Geographically Weighted Regression. R package version 0.6‐34.https://CRAN.R‐project.org/package=spgwr.
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