Exploring Spatial Variation of the Bike Sharing Ridership: A Study Based on Semi-Parametric Geographically Weighted Regression

As an important part of urban public transport system, bike sharing systems are adopted by many cities due to its contribution to energy saving and mitigation of traffic congestion. Understanding factors that influence bike sharing ridership and accurate estimation of ridership at different locations play an important role in determining location of stations and could provide reference for making policies to increase bike sharing ridership. This study divides the ridership into three types: trip production of members, trip attraction of members, and trips of 24-hour pass users, and explored factors that influence the three types of ridership. Previous studies assume the relationship between predicting variables and response variables are the same across the study area. The authors test this assumption by employing semi-parametric geographically weighted regression (S-GWR) model to fit the data and found that the relationship between some predicting variables and response variable are local while other relationships are global. Results show that S-GWR models have better goodness-of-fit than ordinary least squares (OLS) models and can eliminate the autocorrelation in the residuals, which is present in the OLS models. As a result, spatially varying relationship between ridership and influencing factors should be considered when designing bike sharing system.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ANF20 Standing Committee on Bicycle Transportation.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Pu, Li
    • Zhang, Xiaojia
    • Ling, Ziwen
    • Yang, Hongtai
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 5p

Subject/Index Terms

Filing Info

  • Accession Number: 01697858
  • Record Type: Publication
  • Report/Paper Numbers: 19-05119
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:40AM