Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros

Li, Wenxiang; Chen, Shawen; Dong, Jieshuang; Wu, Jingxian

doi:10.1016/j.jtrangeo.2021.103032

Cited by 57 publications

(37 citation statements)

References 27 publications

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“…Bao et al [37] employed K-means clustering method to classify the bikeshare stations into five categories, and then applied five separate GWR models for each station category and compared with the joint model of all station categories, indicating that the prediction performance of separate bikeshare ridership models was generally better than that of the joint model. Li et al [10] used a GWR model to analyze the spatial variation in the relationship between bike-sharing transfer distance and explanatory variables in Shanghai, and showed that transfer distance was related to factors such as daily patronage of metro stations, daily patronage of bike-sharing, population density, parking lot density, distance to central business district (CBD) and bus stop density. In addition, some studies found significant correlations between bike-sharing usage and eye-level greenness and frequency of public transport use [16,18].…”

Section: Literature Reviewmentioning

confidence: 99%

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Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage

Shang

Zhao

et al. 2022

IJERPH

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Dockless bike-sharing systems have become one of the important transport methods for urban residents as they can effectively expand the metro’s service area. We applied the ordinary least square (OLS) model, the geographically weighted regression (GWR) model and the multiscale geographically weighted regression (MGWR) model to capture the spatial relationship between the urban built environment and the usage of bike-sharing connected to the metro. A case study in Beijing, China, was conducted. The empirical result demonstrates that the MGWR model can explain the varieties of spatial relationship more precisely than the OLS model and the GWR model. The result also shows that, among the proposed built environment factors, the integrated usage of bike-sharing and metro is mainly affected by the distance to central business district (CBD), the Hotels-Residences points of interest (POI) density, and the road density. It is noteworthy that the effect of population density on dockless bike-sharing usage is only significant at weekends. In addition, the effects of the built environment variables on dockless bike-sharing usage also vary across space. A common feature is that most of the built environment factors have a more obvious impact on the metro-oriented dockless bike-sharing usage in the eastern part of the study area. This finding can provide support for governments and urban planners to efficiently develop a bike-sharing-friendly built environment that promotes the integration of bike-sharing and metro.

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Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, some studies have shown that cycling can help improve physical and mental health [7][8][9]. In short, dockless bike-sharing systems can bring many social, economic, and environmental benefits to cities [10].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage

Shang

Zhao

et al. 2022

IJERPH

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“…Therefore, the use of data from one month five years ago can provide accurate and useful information. Furthermore, there are also relevant studies that use data from five years ago to analyze the impact of various factors on transfer [54].…”

Section: Study Areamentioning

confidence: 99%

How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

Pian

et al. 2022

Sustainability

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Understanding the determinants of transfer ridership is important for providing insights into improving the attractiveness of transit systems and building reliable and resilient metro stations. This study focuses on the transfer ridership between bus and metro systems under different dates and severe weather conditions to quantify the impacts of various attributes on the transfer ridership of different transfer modes (metro-to-bus and bus-to-metro). A multivariate generalized Poisson regression (GPR) model is applied to investigate the effects of critical factors on the transfer ridership of different transfer modes on weekdays, holidays, and typhoon days, respectively. The results indicate that the transfer-related variables, real-time weather, socioeconomic characteristics, and built environment significantly affect the transfer ridership. Concretely, the influence of socioeconomic and demographic factors on transfer ridership is the most significant on different types of dates, which is approximately 1.19 to 9.28 times that of the other variables. Weather variables have little effect on transfer ridership on weekdays, but they have a more significant impact on the transfer ridership on holidays and typhoon days. Specifically, during typhoons, transfer ridership is more affected by the weather factors: the coefficients are about 2.36 to 4.74 times higher than that in the other periods. Moreover, under strong wind speed, heavy rain, and high-temperature conditions, transfer ridership of the metro-to-bus mode significantly increases. In contrast, transfer ridership of the bus-to-metro mode rapidly decreases. Additionally, the peak hours have a strong positive influence on the transfer ridership, and the average hourly transfer ridership during peak hours is 1.16 to 4.02 times higher than that during the other periods. These findings indicate that the effect of each factor on transfer ridership varies with dates and transfer modes. This can also provide support for improving metro stations and increasing the attractiveness of public transport.

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“…In general, the BSS has been facilitated to connect with public transit services by providing the docks near the transit stops (Li et al, 2021). The BSS generally consists of three components, which are stations, bikes, and applications.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Route Choice Behaviors of Bike-Sharing Users as First- and Last-mile Trips for Introduction of Mobility-as-a-Service (MaaS)

Cho

Shin

2022

KSCE J Civ Eng

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Bicycling has been considered the sustainable alternatives of first-and last-mile trips for Mobility-as-a-Service (MaaS) in integrated transport systems. This study aims to analyze the route choice behaviors of bike-sharing users for the first-and last-mile trips in MaaS, incorporating the connectivity to the subway stations. The observed GPS data is employed to generate the route travels of bike-sharing users, and the choice set generation algorithm, i.e., the k-medoids clustering method, is applied to extract the representative alternatives in route choice models. This study analyzes the path-size logit model to compare the route choice behaviors incorporating peak periods, and access and egress trips. The bike-sharing use at the evening-peak period is more frequent than the other periods. Also, the bike-sharing users are found to be positively associated with the ratio of bike lanes for the access and egress trips to the subway stations, whereas they are reluctant to detour for their trips. The finding of this research is that the extension of bike facilities on access and egress to the subway stations is necessary to improve the bike-sharing use for the first-and last-mile trips in MaaS. Bike-sharing system Global positioning system (GPS) data Mobility-as-a-Service (MaaS) First-and last-mile Route choice behavior Access and egress trip CORRESPONDENCE Shin-Hyung Cho

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Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros

Cited by 57 publications

References 27 publications

Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage

Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage

How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

Estimation of Route Choice Behaviors of Bike-Sharing Users as First- and Last-mile Trips for Introduction of Mobility-as-a-Service (MaaS)

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