Evaluation of Dockless Bike-Sharing Transfer Services around Metro Stations considering Spatial Heterogeneity

Abstract

The dockless bike-sharing system provides a convenient transfer option for metros, which can solve the “last mile” problem for passengers. However, the quality of bike-sharing transfer services around metro stations and its influencing factors are still unrevealed. Therefore, this study aims to evaluate the bike-sharing transfer services for different metro stations considering spatial heterogeneity. First, the transfer ridership of bike-sharing around each metro station is calculated based on the trip data of the Mobike in Shanghai. Then, a geographically weighted regression (GWR) model is used to analyze the relationships between the transfer ridership and built environment features around each metro station. Given the GWR model, a spatially varying benchmark is established to independently evaluate the bike-sharing transfer service of each metro station based on the deviation between the actual transfer ridership and its benchmark. In this case, the metro stations whose actual bike-sharing transfer ridership is below the benchmark are identified as the objects that need to improve their bike-sharing transfer services. The results show that the benchmark derived from the GWR model varies from station to station depending on the metro ridership, population density, and cycleway density around the metro station. In addition, bike-sharing transfer services around metro stations are better in Xuhui District, Minhang District, and Huangpu District, and worse in Pudong District and Qingpu District. Therefore, this study is helpful for the government and operators to better optimize the connection between dockless bike-sharing and metros.

Comparison of the Workday and Non-Workday Carbon Emission Reduction Benefits of Bikeshare as a Feeder Mode of Metro Stations

“…Spatial constraint: There is no standardization in the size of the metro station transfer range between different cities in the existing studies [18,30,33,34]. Differences exist in the frequency of bikeshare transfer use in different cities, and the spatial characteristics of the city also affect the traveler's choice of transport mode.…”

“…Differences exist in the frequency of bikeshare transfer use in different cities, and the spatial characteristics of the city also affect the traveler's choice of transport mode. Based on the existing research [34], the actual travel data of bikeshare in Shenzhen were used to establish the radius of the metro station transfer range, ranging from 50 m to 300 m. Within this range, the amount of bikeshare ridership was counted every 10 m, and the growth rate of the ridership between neighboring transfer ranges was calculated. A line graph was then plotted, and the value corresponding to the turning point, where the growth rate changes from a steep increase to a flat increase, was taken as the radius of the metro station transfer range.…”

Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

“…Te feeder mode choice for metro access and egress is also afected by the characteristics of travel [5,22]. Walking, bicycling, and bus are the most common feeding mode for metro access and egress [8,23,24]. Walking is considered as the most important way to feed the metro.…”

“…To promote synergistic multimodal transportation, many studies also explored the impacts of built environment factors on integrated bicycle-metro use. Metro stations near city centre, with higher land use mix, and more recreational and residential land, are more likely to generate more integrated bike-metro use [24,50,51]. Jin et al investigated the nonlinear relationship between the built environment and metro-ridesourcing integration, showing higher integrated usage in the suburban areas [7].…”