Search Results (382)

The sharing economy is considered to be a potentially efficacious approach for promoting sustainable production and consumption (SPC). This study utilizes dockless bike sharing (DBS) in Beijing as a case study to examine how sharing economy policies and business practices advance SPC. It also dynamically quantifies the environmental and economic performance of DBS practices from a life cycle perspective. The findings indicate that effective SPC practices can be achieved through the collaborative efforts of multiple stakeholders, including the government, operators, manufacturers, consumers, recycling agencies, and other business partners, supported by regulatory systems and advanced technologies. The SPC practices markedly improved the sustainability of DBS promotion in Beijing. This is evidenced by the increase in greenhouse gas (GHG) emission reduction benefits, which have risen from approximately 35.81 g CO₂-eq to 124.40 g CO₂-eq per kilometer of DBS travel. Considering changes in private bicycle ownership, this value could reach approximately 150.60 g CO₂-eq. Although the economic performance of DBS operators has also improved, it remains challenging to achieve profitability, even when considering the economic value of the emission reduction benefits. In certain scenarios, DBS can maximize profits by optimizing fleet size and efficiency, without compromising the benefits of emission reductions. The framework of stakeholder interaction proposed in this study and the results of empirical analysis not only assist regulators, businesses, and the public in better understanding and promoting sustainable production and consumption practices in the sharing economy but also provide valuable insights for achieving a win-win situation of platform profitability and environmental benefits in the SPC practice process. Full article

Society’s adaptation to shared mobility services is a growing topic that requires detailed understanding of the local circumstances of potential and current users. This paper focuses on analyzing barriers to the adoption of urban bike-sharing systems in post-industrial cities, using a case study of the Silesian agglomeration in Poland. Methodologically, the article integrates quantitative survey methods with multivariate statistical analysis to analyze the demographic, socioeconomic, and motivational factors that underline the adoption of shared micromobility. The study highlights a detailed segmentation of users by income, age, professional status, and gender, as well as the observation of profound disparities in access and perceived usefulness. Of note is the study’s identification of a highly concentrated segment of young, low-income users (mostly students), which largely accounts for the general perception of economic and infrastructural barriers. These include the use of factor analysis and regression to plot the interaction patterns between individual user characteristics and certain system-level constraints, such as cost, infrastructure coverage, weather, and health. The study’s findings prioritize problem-specific interventions in urban mobility planning: bridging equity gaps between user groups. This research contributes to the current literature by providing detailed insights into the heterogeneity of user mobility behavior, offering evidence-based recommendations for inclusive and adaptive options for shared transportation infrastructure in a changing urban context. Full article

Bikeshare systems usually relocate bikes to respond to a mismatch between demand and bike supply, imposing substantial costs to operators despite the effort to encourage users to participate in voluntary rebalancing. This study initiates a search for a new strategy that can involve single station-based (SSB) riders and consider their bikes as the reserve of the current bike balance, resulting in the virtual expansion of station capacity. Thus, the behaviors of bike riders related to SSB trips are compared to investigate the potential applications. The results from analyzing the data of Citi Bike in New York City indicate that 13.4% of total trips were SSB, and the average trips per origin and destination (OD) pair was 2.6 times higher. Also, distinctive characteristics such as mean trip time regarding user groups and bike types were statistically significant within numerous OD pairs, implying the need for separate policies for both groups. Based on the analysis, stations with the highest expected benefit are identified. Full article

This study investigates public perceptions of sustainable mobility within university environments, which are important trip generation hubs with the potential to influence and disseminate sustainable mobility behaviors. Using sentiment analysis on 120,236 tweets from São Paulo, Rio de Janeiro, Lisbon, and Porto, tweets were classified into positive, neutral, and negative sentiments to assess perceptions across transport modes. It was hypothesized that universities would exhibit more positive sentiment toward active and public transport modes compared to perceptions of these modes within the broader city environment. Results show that active modes and public transport consistently receive higher positive sentiment rates than individual motorized modes, and, considering the analyzed contexts, universities demonstrate either similar (São Paulo) or more positive perceptions compared to the overall sentiment observed in the city (Rio de Janeiro, Lisbon, and Porto). Chi-square tests confirmed significant associations between transport mode and sentiment distribution. An exploratory analysis using topic modeling revealed that perceptions around bicycle use are linked to themes of safety, cycling infrastructure, and bike sharing. The findings highlight opportunities to promote sustainable mobility in universities by leveraging user sentiment while acknowledging limitations such as demographic bias in social media data and potential misclassification. This study advances data-driven methods to support targeted strategies for increasing active and public transport in university settings. Full article

As climate change concerns, urban congestion, and environmental degradation intensify, cities prioritise cycling as a sustainable transport option to reduce CO₂ emissions and improve quality of life. However, rampant bicycle theft and poor security infrastructure often deter daily commuters and tourists from cycling. This study explores how advanced security measures can bolster sustainable urban mobility and tourism by addressing these challenges. A mixed-methods approach is utilised, incorporating primary survey data from Slovenia and secondary data on bicycle sales, imports and thefts from 2015 to 2024. Findings indicate that access to secure parking substantially enhances users’ sense of safety when commuting by bike. Regression analysis shows that for every 1000 additional bicycles sold, approximately 280 more thefts occur—equivalent to a 0.28 rise in reported thefts—highlighting a systemic vulnerability associated with sustainability-oriented behaviour. To bridge this gap, the study advocates for an innovative security framework that combines blockchain technology and Non-Fungible Tokens (NFTs) with encrypted Quick Response (QR) codes. Each bicycle would receive a tamper-proof QR code connected to a blockchain-verified NFT documenting ownership and usage data. This system facilitates real-time authentication, enhances traceability, deters theft, and builds trust in cycling as a dependable transport alternative. The proposed solution merges sustainable transport, digital identity, and urban security, presenting a scalable model for individual users and shared mobility systems. Full article

This paper proposes a general workflow for collecting and analyzing open data from Bicycle Sharing Systems (BSSs) that was developed using data from the Valenbisi system, operated in Valencia by the French company JCDecaux; however, the stages of the proposed workflow are service-independent and can be applied broadly. Cycling has become an increasingly popular mode of transportation, leading to the emergence of BSSs in modern cities. Parallel to this, Smart City solutions have been implemented using Internet of Things (IoT) technologies, such as embedded sensors and GPS-based communication systems, which have become essential to everyday life. When public transportation services or bicycle sharing systems are used, real-time information about the services is provided to customers, including vehicle tracking based on GPS technology and the availability of bikes via sensors installed at bike rental stations. The bike stations were examined from two different perspectives: first, their daily usage, and second, the types of facilities located in their surroundings. Based on these two approaches, the overlap between the clustering results was analyzed—specifically, the similarity in how stations could be grouped and the correlation between their usage and locations. To enhance the raw data retrieved from the service provider’s official API, the stations were annotated based on OpenStreetMap and Overpass API data. Data visualization was created using Tableau from Salesforce. Based on the results, an agreement of 62% was found between the results of the two different clustering approaches. Full article

Bike-sharing has been widely recognized for addressing the “last-mile” problem and improving commuting efficiency. While prior studies emphasize how the built environment shapes feeder trips, the effects of station types and spatial heterogeneity on bike-sharing and metro integration remain insufficiently explored. Taking the urban core area of Shenzhen as a case study, this paper examines how the built environment influences such integration during morning peak hours and how these impacts differ across station types. First, we proposed a “3Cs” (convenience, comfort, and caution) framework to capture key built environment factors. Metro stations were classified into commercial, residential, and office types via K-means clustering. Subsequently, the ordinary least squares (OLS) regression model and the multiscale geographically weighted regression (MGWR) model were employed to identify significant factors and explore the spatial heterogeneity of these effects. Results reveal that factors influencing bike-sharing–metro integration vary by station type. While land-use mix and enclosure affect bike-sharing usage across all stations, employment and intersection density are only significant for commercial stations. Furthermore, these influences exhibit spatial heterogeneity. For instance, at office-oriented stations, population shows both positive and negative effects across areas, while residential density has a generally negative impact. These findings enhance our understanding of how the built environment shapes bike-sharing–metro integration patterns and support more targeted planning interventions. Full article

The aim of this paper is to develop a case study of the recent situation of a bike-sharing service in a chosen small urban area. Žilina is situated in northern Slovakia, with a population exceeding 80,000 and an area of 80.03 km². This study represents a complex analysis of the available data on a bike-sharing service, as well as data on bicycle rentals from a local provider. Both were processed by the QGIS software. First, the number of rentals and the attractiveness of the bicycle stations were evaluated, taking into account the seasons from 2019 to the end of the 2023 season. Spatial analysis, based on marking the availability of the isochrones of the 32 bike-sharing stations at the end of the season 2024, was conducted considering the map’s characteristics. The analysis was supplemented with a questionnaire survey of bike-sharing service users. This study provides an overall view of the recent situation of a bike-sharing service operating for five years in a small urban area with the intention of identifying deficiencies and improving the service for future system expansion. The originality of this paper lies in the processing of a wide dataset with an extensive set of control variables and the connection of spatial and non-spatial analyses. The approaches and results can serve as proposals for introducing or designing bike-sharing services in other small urban areas for researchers. Full article

This paper investigates the competition between two types of bike-sharing services, particularly at bus stops, subway stations, and residential areas. Two types of shared bicycle travel choice models are constructed. A shared bicycle operator attracts users by implementing discount incentives, and the comfort levels of riding the two types of shared bicycles are different. The equilibrium fares, potential user demand, and operator profits under joint profit maximization, price competition, and potential user demand competition scenarios are derived, and the competitive results under the three scenarios are compared. The results show that, in the potential user demand competition, the difference in potential demand between the two operators is largest; in the joint profit maximization scenario involving shared bicycle operators, the difference in potential user demand is smallest. In all competitive scenarios, higher operating costs and costs in lowering comfort loss for the shared bicycle operators will increase fares; the substitution level between the two types of shared bicycles has a positive impact on potential user demand, and the higher the substitution level, the better the effect of discounts in attracting users. Full article

This paper aims to propose innovative solutions to improve the management of Barcelona’s bike-sharing system, known as Bicing. This study addresses one of the system’s main challenges: the unequal distribution of bicycles across the city and at different times of the day, which affects the users. The analysis combines advanced statistical techniques, predictive models and optimization algorithms to identify vulnerable areas in terms of accessibility and design strategies to balance bicycle distribution. Using methods such as clustering and predictive models based on machine learning, the system’s usage patterns are anticipated. These predictions feed optimization algorithms that enable the planning of more efficient routes for bicycle repositioning, reducing unnecessary vehicle movement and supporting a more environmentally friendly mobility network. The results highlight the importance of proactive system management, improving both user satisfaction and operational efficiency while fostering a more sustainable urban transport ecosystem. Full article

With increasing concerns over climate change and air pollution, sustainable transportation has become a critical component of modern city planning. Bike-sharing systems have emerged as an eco-friendly alternative to motorized transport, contributing to energy conservation and emission reduction. To elaborate on bike-sharing’s contribution to urban sustainable development, this study conducts a quantitative analysis of its environmental benefits through a case study of the Bluebikes program in the Boston area, using a longitudinal dataset of 20.07 million bike trips from January 2015 to December 2024, with data between January 2020 and December 2021 excluded. A combination of Scheiner’s model and Multinomial Logit model was adopted to evaluate the substitution of Bluebikes trips, an optimized Seasonal Autoregressive Integrated Moving Average (SARIMA) model was employed to predict future usage, while energy savings were calculated by estimating reductions in gasoline and diesel consumption. The findings reveal that during the analyzed period, Bluebikes trips saved 2616.44 tons of oil equivalent and reduced CO₂ and NO_X emissions by 7614.96 and 16.43 tons, respectively. Furthermore, based on the historical trends, it is forecasted that the Bluebikes program will annually save an average of 723.66 tons of oil equivalent and decrease CO₂ and NO_X emissions by 2422.65 and 4.52 tons between 2025 and 2027. The results highlight the substantial environmental impact of Bluebikes and support policies that encourage their usage. Full article

The rapid growth of e-bikes has intensified traffic conflicts on slow-moving shared paths in China. This study analyzed traffic safety of pedestrians and non-motorized vehicles and examined the factors influencing conflict severity utilizing traffic conflict techniques. Video-based surveys were conducted on six shared paths in Shenzhen, and conflict trajectory was extracted by Petrack software (Version 0.8). The minimum Time to Collision and Yaw Rate Ratio were selected as conflict indicators. Fuzzy c-means clustering was employed to classify conflicts into three severity levels: 579 potential conflicts, 435 minor conflicts, and 150 serious conflicts. Nineteen feature variables related to road environment, traffic operation, conflict sample information, and conflict behavior were considered. A SMOTE random forest model was constructed to explore critical influencing factors systematically. The results identified ten key factors affecting conflict severity. The increase in conflict severity is associated with the rise in pedestrian proportion and flow, and the decrease in e-bike proportion and flow. Male participants and pedestrians are more likely to engage in serious conflicts, while illegal lane occupation and wrong-way travel further elevate the severity level. These findings can provide references for traffic engineers and planners to enhance the safety management of shared paths and contribute to sustainable non-motorized transport. Full article

Bike-sharing initiatives play a crucial role in sustainable urban transportation, addressing vehicular congestion, air quality issues, and sedentary lifestyles. However, the connection between bike-sharing facilities and the advantages perceived by users remains insufficiently explored particular in post-industrial regions, such as Silesia, Poland. This study develops a multidimensional framework linking infrastructure elements—such as station density, bicycle accessibility, maintenance standards, and technological integration—to perceived benefits. Using a mixed-methods approach, a survey conducted in key Silesian cities combines quantitative analysis (descriptive statistics, factor analysis, and regression modelling) with qualitative insights from user feedback. The results indicate that the most valuable benefits are health improvements (e.g., improved physical fitness and mobility) and environmental sustainability. However, infrastructural deficiencies—disjointed bike path systems, uneven station placements, and irregular maintenance—substantially hinder system efficiency and accessibility. Inadequate bike maintenance adversely affects efficiency, safety, and sustainability, highlighting the necessity for predictive upkeep and optimised services. This research underscores innovation as a crucial factor for enhancing systems, promoting seamless integration across multiple modes, diversification of fleets (including e-bikes and cargo bikes), and the use of sophisticated digital solutions like real-time tracking, contactless payment systems, and IoT-based monitoring. Furthermore, the transformation of post-industrial areas into cycling-supportive environments presents strategic opportunities for sustainable regional revitalisation. These findings extend beyond the context of Silesia, offering actionable insights for policymakers, urban mobility planners, and Smart City stakeholders worldwide, aiming to foster inclusive, efficient, and technology-enabled bike-sharing systems. Full article

Bikeshare has emerged as a sustainable mobility solution not only for addressing the first- and last-kilometer problem but facilitating short- and medium-distance travel. While existing research predominantly focuses on city-level Bikeshare Programs (BSPs), there is a paucity of studies examining university campus BSPs, particularly in terms of quantitative analysis of trip frequency and system operation sustainability. This paper presents a systematical framework to investigate university campus BSPs from two complementary perspectives: users’ travel characteristics and operational sustainability. To achieve this, two successive self-reported questionnaire surveys were conducted on the campus of South China University of Technology in 2017 and 2020, respectively. Subsequently, a multinomial logistic regression model was developed to identify the key factors influencing users’ travel frequency. Finally, a cost–benefit analysis was developed to assess the operational sustainability of the system. The findings reveal two significant insights: (1) the system was profitable under the 2017 fare policy, with the potential to maximize profits by strategically increasing fares while enhancing service quality; and (2) in 2020, when the fare is adjusted closer to the predicted optimal value, there is an increase in the proportion of high-frequency users, accompanied by improved user experience, reduced difficulty in bike access/return, and slightly lower pricing satisfaction. This study provides a valuable method that can be extended to the restricted service communities for effective planning and evaluation of bikeshare systems. Full article

Climate change is an urgent issue, and the current mindset of private ownership, particularly of private vehicles, needs to shift. Shared mobility is rapidly emerging as a key part of the solution to contemporary transportation challenges, driven by technological advancements and the growing demand for more sustainable travel options. This paper provides a comprehensive analysis of shared mobility operational models in Europe, focusing on carsharing and its current research on fleet optimization, bikesharing, and scooter sharing. The study draws on three scientific literature databases, with searches centered on keywords relevant to Shared Mobility. This study contributes to the literature by defining each Shared Mobility modality and examining the different operational models. Full article