01697858

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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.

This paper was sponsored by TRB committee ANF20 Standing Committee on Bicycle Transportation.

19-05119

English

5p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Bicycle travel; Bicycles; Regression analysis; Spatial analysis; Urban transportation; Vehicle sharing

Geographically weighted regression; Ordinary least-squares

Pedestrians and Bicyclists; Planning and Forecasting

Transportation Research Board Annual Meeting 2019 Paper #19-05119

TRIS, TRB, ATRI

Dec 7 2018 9:40AM