scholarly journals Are you being shared? Mobility, data and social relations in Shanghai’s Public Bike Sharing 2.0 sector

2018 ◽  
Vol 3 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Justin Spinney ◽  
Wen-I Lin
Keyword(s):  
Social Relations ◽  
Mobility Data ◽  
Shared Mobility ◽  
Bike Sharing

scholarly journals A Holistic Approach to Electric Shared Mobility Systems Development—Modelling and Optimization Aspects

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5810 ◽  
Author(s):  
Katarzyna Turoń ◽  
Andrzej Kubik ◽  
Feng Chen ◽  
Hualan Wang ◽  
Bogusław Łazarz
Keyword(s):  
Electric Vehicles ◽  
Service Providers ◽  
Holistic Approach ◽  
Analysis Method ◽  
Limited Effect ◽  
Modeling And Optimization ◽  
The Social ◽  
Shared Mobility ◽  
Bike Sharing ◽  
Modelling And Optimization

Due to the development of the shared economy, increasingly more shared mobility providers have launched services based on the use of electric vehicles. The increasing growth of electric shared mobility services has produced various types of problems that do not occur (or occur with a limited effect) under conventional shared mobility systems. This increase in electric shared mobility problems has led to many effects, including limitations of the system zones or going out of business. To avoid difficulties in the functioning of electric shared mobility systems, various scientific studies have been undertaken to model and optimize the operation of these systems. Modeling and optimization mainly relate to one category of the system—for example, only to bike sharing. However, to understand the system of electric shared mobility holistically, there is a need to define the criteria generally as stimulants or destimulants. Based on these assumptions, we conducted research on the identification of factors influencing the development of electric shared mobility services. We conducted our own expert research based on the Social Network Analysis method. The aim of this study was to determine the factors that influence the development or recession of services in the entire electric shared mobility market in reference to selected stakeholders. The obtained results indicate a non-standard approach to the modeling and optimization of electric shared mobility services. This study could be used as support for creating electric shared mobility models and could also be helpful for service providers or local mobility managers through the developed recommendations.

scholarly journals Electric Scooter Sharing and Bike Sharing User Behaviour and Characteristics

2020 ◽  
Vol 12 (22) ◽  
pp. 9640
Author(s):  
Tomasz Bieliński ◽  
Agnieszka Ważna
Keyword(s):  
Gender Gap ◽  
Travel Behaviour ◽  
Disabled People ◽  
User Behaviour ◽  
Electrically Assisted ◽  
Last Mile ◽  
Electric Scooter ◽  
Shared Mobility ◽  
The World ◽  
Bike Sharing

New, shared mobility modes, including dockless e-scooters and e-bikes, were recently introduced to many cities around the world. The aim of this article is to determine the differences between the users of e-bike sharing, and e-scooter sharing systems, and the characteristics of their travel behaviour. This study is based on the survey of the citizens of Tricity in northern Poland. We find that e-bicycles are predominantly used as first and last mile transport and to commute directly to various places of interest, whereas e-scooters are more often used for leisure rides. Survey respondents that adopted shared micromobility are generally young, and e-scooter users are on average younger than e-bike users. Although all shared vehicles in Tricity are electrically assisted, this did not allow for the elimination of the gender gap, or help retired and disabled people in the adoption of shared micromobility services. We have also identified factors discouraging people from the usage of e-bike and e-scooter sharing and found them to be different for both types of services. Finally, we investigated the issue of using shared e-bikes for urban logistics.

Unifying Online and Offline Preference for Social Link Prediction

2021 ◽  
Author(s):  
Fan Zhou ◽  
Kunpeng Zhang ◽  
Bangying Wu ◽  
Yi Yang ◽  
Harry Jiannan Wang
Keyword(s):  
Neural Network ◽  
Social Relations ◽  
Link Prediction ◽  
Factor Model ◽  
Representation Learning ◽  
Machine Learning Algorithms ◽  
Locality Sensitive Hashing ◽  
Network Representation ◽  
Mobility Data ◽  
Social Link

Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance. Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science.

scholarly journals Promoting User Participation of Shared Mobility in the Sharing Economy: Evidence from Chinese Bike Sharing Services

2021 ◽  
Vol 13 (3) ◽  
pp. 1533
Author(s):  
Liguo Lou ◽  
Lin Li ◽  
Sung-Byung Yang ◽  
Joon Koh
Keyword(s):  
Social Exchange ◽  
User Satisfaction ◽  
Structural Equation ◽  
Critical Role ◽  
Sharing Economy ◽  
User Participation ◽  
Ease Of Use ◽  
The Body ◽  
Shared Mobility ◽  
Bike Sharing

User participation plays a critical role in the business success of shared mobility services. This study classifies user participation behavior into two different types (in- and extra-role participations), integrates the motivation–opportunity–ability (MOA) model and social exchange theory (SET) to identify key antecedents, and empirically examines the influences of user–user, user–provider, and user–service interaction-related factors on user participation in the context of bike sharing services. The results of structural equation model analysis with 438 bike sharing service users in China reveal that altruism, rewards, and user knowledge enhance both in- and extra-role participations, whereas perceived ease of use promotes only user in-role participation, and both user satisfaction and commitment increase only user extra-role participation. Rewards are also found to promote user satisfaction, ultimately increasing user commitment. This study contributes to the body of knowledge on value co-creation and customer cooperation behavior in the sharing economy and provides practical implications to both managers of bike sharing services and policymakers for urban transportation and ICT-enabled sustainable development.

scholarly journals Mobilising the dispositive: Exploring the role of dockless public bike sharing in transforming urban governance in Shanghai

Urban Studies ◽  
2020 ◽  
pp. 004209802093794
Author(s):  
Wen-I Lin ◽  
Justin Spinney
Keyword(s):  
Social Relations ◽  
Urban Governance ◽  
Internal Dynamics ◽  
Bike Sharing ◽  
Virtual Mobility ◽  
Hierarchical Relationships

This paper contributes to debates on urban governance and mobility through a case study of the transformation of public bike sharing schemes in Shanghai (China) from fixed/docked (PBSS 1.0) to flexible/dockless (PBSS 2.0). Based upon stakeholder interviews and observations between 2015 and 2017, we use the concept of a dispositive to foreground two related processes. The first is the reformulation of the governmental dispositive that coalesces around PBSS in Shanghai. We show how the relations within the dispositive shift from more hierarchical, bounded, regulated and state-led to those characterised by a more dispersed, disconnected, horizontal and distant set of social relations. Second, we show how this dispositive both produces and is produced by an emergent environmentality that manifests in a fixed territoriality in PBSS 1.0 and a more fluid and deterritorialised digital environmentality in PBSS 2.0. In framing this shift, we demonstrate how PBSS 2.0 produces a new dispositive of urban governmentality where the conduct of users is dispersed through a much less co-ordinated network of actors and technologies. Ultimately we argue that it is no longer possible to separate physical and virtual mobility when trying to understand the internal dynamics and external manifestations of mobility governance, which in our example are characterised by less localised and less hierarchical relationships that are more fluid, voluntary and physically distant.

Assessing and Predicting Mobility Improvement of Integrating Bike-Sharing into Multimodal Public Transport Systems

2021 ◽  
pp. 036119812110450
Author(s):  
Christian Kapuku ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Machine Learning ◽  
Transport Systems ◽  
Mobility Performance ◽  
Smart Card Data ◽  
Machine Learning Model ◽  
Multimodal Transport ◽  
Shared Mobility ◽  
Bike Sharing ◽  
The City ◽  
Better Than

New shared mobility services have become increasingly common in many cities and shown potential to address urban transportation challenges. This study aims to analyze the mobility performance of integrating bike-sharing into multimodal transport systems and develop a machine learning model to predict the performance of intermodal trips with bike-sharing compared with those without bike-sharing for a given trip using transit smart card data and bike-sharing GPS data from the city of Seoul. The results suggest that using bike-sharing in the intermodal trips where it performs better than buses could enhance the mobility performance by providing up to 34% savings in travel time per trip compared with the scenarios in which bus is used exclusively for the trips and up to 33% savings when bike-sharing trips are used exclusively. The results of the machine learning models suggest that the random forest classifier outperformed three other classifiers with an accuracy of 90% in predicting the performance of bike-sharing and intermodal transit trips. Further analysis and applications of the mobility performance of bike-sharing in Seoul are presented and discussed.

Analysis of the Integration Usage Patterns of Multiple Shared Mobility Modes and Metro System

2021 ◽  
pp. 036119812110133
Author(s):  
Hui Bi ◽  
Zhirui Ye ◽  
Yi Zhang
Keyword(s):  
Weather Conditions ◽  
Sustainable Transportation ◽  
Urban Transport ◽  
Policy Implications ◽  
Transport Systems ◽  
Sustainable Mobility ◽  
Long Distance ◽  
Metro Station ◽  
Shared Mobility ◽  
Bike Sharing

Although metro systems are established in many Asian cities including Chengdu, they have yet to cover every corner of a city. Understanding the transfer behavior of passengers can provide insight into achieving efficient and sustainable urban transport systems. Combining shared mobility programs with metro to improve the weaknesses of traditional feeder modes is viewed as the most promising line of business in sustainable transportation for the near future. Therefore, this study aims to comprehend the factors affecting the usage regularity of shared mobility by deepening the knowledge on endogenous and exogenous effects, and integrating two modes, namely bike-sharing and ridesourcing. Two systems are cross-compared, first in respect of their travel characteristics. Then, a binary logistic model is employed to capture the influences of trip characteristics and travel environment characteristics on their usage frequency. Researchers found that trip distance is significantly associated with users’ mode options, indicating that bike-sharing and ridesourcing mainly serve short-distance and long-distance transfer users, respectively, although some users may be confused which feeder mode to choose for the journeys of 2 km to 4 km. There were also meteorological and temporal influences, with the competition and complementation of multiple shared mobility feeder modes being likely to change under extreme weather conditions, during peak hours, or on weekends. Besides, metro-shared mobility users value the accessibility of two kinds of transport service, which is affected by the metro station and its surrounding built environment. This study and the proposed policy implications are helpful for embracing a sustainable mobility design from general optimum.

scholarly journals Emerging Technologies and Methods in Shared Mobility Systems Layout Optimization of Campus Bike-Sharing Parking Spots

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Tangyi Guo ◽  
Jie Yang ◽  
Liu He ◽  
Kun Tang
Keyword(s):  
Travel Time ◽  
Traffic Management ◽  
Rapid Development ◽  
Construction Cost ◽  
Study Analysis ◽  
Parking Spot ◽  
Average Travel Time ◽  
Shared Mobility ◽  
Bike Sharing ◽  
Scientific Decision

The rapid development of bike sharing has posed some challenges to the traffic management on campus. The bike sharing on campus has problems such as messy parking, and some buildings in the peak hours have no bikes to borrow. Thus, alternative parking spots are proposed based on the layout principle of parking spots for bicycles. An optimization model of the layout of campus bike-sharing parking spots with travel time and construction cost as the optimization goal is established, and the branch and bound algorithm is used to solve the model. Finally, the study analysis is carried out by optimizing the layout of the bike-sharing parking spot of Nanjing University of Science and Technology. The results show that, after optimizing the layout of parking spots, the average travel time of users is reduced by 6.0%, and the total construction cost is reduced by 27.3%. While being convenient for campus bike-sharing users, it also provides scientific decision-making support for the campus traffic management.

scholarly journals Analysis of the Use and Perception of Shared Mobility: A Case Study in Western Australia

2021 ◽  
Vol 13 (16) ◽  
pp. 8766
Author(s):  
Craig Standing ◽  
Ferry Jie ◽  
Thi Le ◽  
Susan Standing ◽  
Sharon Biermann
Keyword(s):  
Western Australia ◽  
Sharing Economy ◽  
Transport Sector ◽  
Age Group ◽  
Factors Affecting ◽  
Number Of Children ◽  
Younger Age ◽  
Shared Mobility ◽  
Bike Sharing

The sharing economy has acquired a lot of media attention in recent years, and it has had a significant impact on the transport sector. This paper investigates the existing impact and potential of various forms of shared mobility, concentrating on the case study of Wanneroo, Western Australia. We adopted bibliometric analysis and visualization tools based on nearly 700 papers collected from the Scopus database to identify research clusters on shared mobility. Based on the clusters identified, we undertook a further content analysis to clarify the factors affecting the potential of different shared mobility modes. A specially designed questionnaire was applied for Wanneroo’s residents to explore their use of shared mobility, their future behaviour intentions, and their perspectives on the advantages and challenges of adoption. The empirical findings indicate that the majority of respondents who had used shared mobility options in the last 12 months belong to the low-mean-age group. The younger age group of participants also showed positive views on shared mobility and would consider using it in the future. Household size in terms of number of children did not make any impact on shared mobility options. Preference for shared mobility services is not related to income level. Bike sharing was less commonly used than the other forms of shared mobility.

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