Diffusion behavior in a docked bike-sharing system

Wei, Xueyan; Luo, Sida

doi:10.1016/j.trc.2019.08.018

Cited by 24 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parkes et al 15 analyzed the speed and extent of BSS in Europe and North America. Wei et al 16 stated that the distribution of bike trip distance follows a strong power law. Li et al 17 used the time‐series clustering method for mining bike usage in BSS.…”

Section: Related Workmentioning

confidence: 99%

Mobility prediction for uneven distribution of bikes in bike sharing systems

Shir

Verma

Bhattacharya

2022

Concurrency and Computation

View full text Add to dashboard Cite

User mobility represents the movement of either individual or group. In smart cities, detection and prediction of mobility patterns are required for numerous applications like resource distribution, traffic management, and user behavioral analysis. With the increase in the number of smart vehicles, urban mobility detection and prediction have become a critical problem for study. Bike-sharing ecosystems (BSS) form an integral part of such ecosystems, as it supports the green revolution, ease of access, and solves traffic problems. However, recent schemes have suggested that BSS are challenged by issues of high density, mobility complexity of bikes (stations), large commute cost, uneven distribution, and route imbalances. To address the critical issues, the article proposes a hybrid scheme that combines rebalancing using clustering that addresses the mobility complexity. Once rebalancing is done, we address the uneven distribution among clusters using prediction models. This article is presented a comparative analysis of algorithms like fuzzy C-means clustering, linear regression, decision tree, and random forest classifiers for predictive analysis performed on weather data and nonweather data. The presented results indicate the viability of the proposed model in real-world scenarios.

show abstract

Section: Related Workmentioning

confidence: 99%

Mobility prediction for uneven distribution of bikes in bike sharing systems

Shir

Verma

Bhattacharya

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Even though dockless systems allow more flexibility for users since there is no specific location for bicycle drop-off, public policymakers tend to avoid them. The main reason is that bicycles are often left on sidewalks and have a higher rate of breaking (Wei, Luo, & Nie, 2019). People typically use this system for short-distance trips, and when integrated adequately with transit, it can potentially offer a solution for the "first-and-last mile" dilemma (Fan, Chen, & Wan, 2019;Wang & Zhou, 2017).…”

Section: Introductionmentioning

confidence: 99%

Moura et al_TransportRxiv_GIRA demand_2022.pdf

Moura¹,

Valença²,

Félix³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>Bike-sharing systems allow occasional and regular users to move by replacing other transport modes for the same trip or generating a new journey. Our research assesses the demand for Lisbon's public dock-based bike-sharing system (BSS) users named after GIRA. This paper aims to identify the determinant factors that influence the potential of the BSS to generate new trips or replace previous modes using a binary logit model based on a survey of 3112 BSS users. The survey results indicate that GIRA generated approximately 20% of the BSS trips, i.e., they would not have been realized if GIRA did not exist. The remaining BSS trips replaced other motorized (55%) and non-motorized (25%) trips. The main determinants explaining a higher likelihood of replacing other modes are having a yearly GIRA pass and a bike-sharing station within a 5-min walking distance. In contrast, regular car users are more likely to generate new trips, suggesting they use bike-sharing for recreational purposes. The findings provide policymakers with an assessment of the determinants that can influence bike-sharing users to generate or substitute trips from other modes for bike-sharing and, consequently, give policies to potentially increase bike-sharing mobility share.</p>

show abstract

“…In today’s construction of the low-carbon society, it has been promoted as an effective solution to the “last mile” problem for. At present, the research on bike-sharing mainly focuses on innovation of business management and profit models [ 1 – 3 ], legal system [ 4 ], influencing factors [ 5 – 7 ]and usage behavior [ 8 , 9 ]. However, the mechanism between the influencing factors and usage intention is seldom researched.…”

Section: Introductionmentioning

confidence: 99%

Understanding the intention to use bike-sharing system: A case study in Xi’an, China

et al. 2021

View full text Add to dashboard Cite

Bike-sharing is widely recognized as an eco-friendly mode of transportation and seen as one of the solutions to the problem of air pollution and congestion. With the emphasis on sustainability in transportation, bike-sharing systems is an emerging topic of urban transport and sustainable mobility related research. Existing studies mainly explored the factors affecting individuals’ initial intentions to start using a shared bicycle, but few looked at the likelihood that a user would continue using one This study proposed a structural equation model with bike-sharing purchase decision involvement as independent variable, bike-sharing willingness to use as dependent variable, traveler participation and traveler perceived value as intermediary variables by introducing the concepts of purchase decision involvement, customer participation and perceived value in consumer psychology and behavior. A survey on bike-sharing users in Xi’an was conducted online and offline, and 622 effective responses were collected. The research model was tested by Amos 24.0 and the empirical results showed that All influencing factors including bike-sharing’s purchase decision-making involvement, traveler participation and traveler’s perceived value are found to be significantly and positively associated with usage intention; traveler perceived value play a chain-mediating role between bike-sharing purchase decision involvement and usage intention; bike-sharing purchase decision involvement have indirect effects on traveler perceived value through traveler participation. The results of this study enrich the current research’s in the field of sharing economy, and it is certain guiding significant for how to obtain and maintain stable customers in bicycle-sharing industry.

show abstract

Diffusion behavior in a docked bike-sharing system

Cited by 24 publications

References 26 publications

Mobility prediction for uneven distribution of bikes in bike sharing systems

Mobility prediction for uneven distribution of bikes in bike sharing systems

Moura et al_TransportRxiv_GIRA demand_2022.pdf

Understanding the intention to use bike-sharing system: A case study in Xi’an, China

Contact Info

Product

Resources

About