Search Results (243)

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

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

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 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

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

Air pollution, largely driven by car traffic, poses significant challenges in many cities, including Košice, Slovakia. As the city explores micromobility as a part of its smart city initiatives and sustainable alternative to individual car use, understanding its spatial dynamics becomes essential. Despite the growing adoption of shared micromobility systems, research on their spatial patterns in Central Europe is still limited. This study analyzes over 900,000 trips made between 2019 and 2022 using bicycles, e-bikes, e-scooters, and e-mopeds in Košice’s dockless system. Using spatial analysis, we identified key hubs near public transport stops, pedestrian zones, and universities, highlighting how micromobility addresses the first/last mile transport challenge. A notable shift from bicycles to e-scooters was observed, enabling wider adoption in areas with fragmented terrain and neighborhoods farther from the city center. Our findings show a significant demand for shared micromobility, indicating its potential to reduce urban car dependency and support smart and sustainable urban transport. However, winter months remain a challenge, with high smog levels but near-zero demand for shared micromobility. Full article

The growing urgency to address urban air quality and climate change has intensified the need for sustainable mobility solutions that mitigate vehicular emissions. Bike-sharing systems (BSSs) represent a viable alternative; however, their precise environmental impact remains insufficiently explored. This study quantifies and forecasts reductions in CO₂ and NO_x emissions resulting from BSS usage in Madrid by integrating real-time IoT sensor data with an advanced predictive model. The proposed framework effectively captures nonlinear and seasonal mobility and emission patterns, achieving high predictive accuracy while demonstrating significant energy savings. These findings confirm the environmental benefits of BSSs and provide urban planners and policymakers with a robust tool to extend and replicate this analysis in other cities, fostering sustainable urban mobility and improved air quality. Full article

This article aims to analyze the use of a bike-sharing system (BSS) during the flooding event caused by extreme rainfall that hit the municipality of Porto Alegre, Brazil, in May 2024. Public transport services were interrupted, prompting an investigation into the resilience of the BSS during the crisis. Considering data from the Tembici BSS company, a set of approximately 400,000 trips made between 104 stations in the municipality of Porto Alegre from January to May 2024 were analyzed. Daily rainfall data from the National Institute of Meteorology (INMET) were compared with the daily trip flow to identify the travel flow patterns on the days most affected by the flooding. The results indicate an abrupt drop in shared bicycle use during May 2024, but 7600 trips were recorded despite the crisis. Regarding the travel pattern between 1 May and 10 May, most trips were still for recreational purposes (73%), while trips for work and study accounted for 22% of the total, and only 5% were for delivery services. Overall, the resilience of the BSS during the extreme climate event in question points to the continuation of practical daily activities, although with more significant effects on economic-related activities and lesser effects on leisure-related activities. Full article

Urban freight transportation is facing significant challenges due to increasing demand, driven by globalization, e-commerce growth, and the adoption of just-in-time logistics. These trends have led to rising vehicle flows in urban areas, negatively impacting sustainability, economic efficiency, and road safety. In response, cities are exploring innovative last-mile delivery strategies that emphasize sustainability, flexibility, and cost efficiency. Among these strategies, cargo bikes—particularly electric cargo bikes (e-cargo bikes)—are emerging as promising low-emission solutions for urban freight distribution. However, despite their potential, a generalized methodology for estimating their demand share in urban contexts remains underdeveloped. This study proposes a comprehensive modelling framework to evaluate the freight demand share that can be addressed by e-cargo bikes, integrating quantity, restocking service, modal, and delivery sub-models, calibrated using data from a case study in Italy. The results demonstrate that e-cargo bikes could fulfil up to 20% of urban freight demand, depending on the category of goods transported, and underscore the feasibility of integrating e-cargo bikes into urban logistics systems. However, critical challenges related to scalability and cost-effectiveness persist, highlighting the need for further research and reliable cost data to support broader implementation. Full article

Governments are encouraging public transportation and bicycle-sharing systems to promote sustainable development and reduce greenhouse gas emissions. Despite the expansion of Taipei’s YouBike program, many stations frequently run out of bikes or docking spaces, and current redistribution strategies are suboptimal. This study proposes a novel approach to optimize YouBike allocation under resource constraints. We first used K-means clustering to group stations with similar rental profiles, reducing the number of models needed. A random forest model selected key crowd grid factors as input variables for a long short-term memory (LSTM) prediction model to accurately predict demand patterns, including during special events or weather changes. A genetic algorithm then determined optimal station configurations and provided return station recommendations, considering user destinations and station dock ratios, while minimizing manual redistribution. Simulations demonstrated that the proposed system meets user needs, enhances operational efficiency, and significantly reduces manual redistribution costs. Our methods have practical applicability for YouBike managers, indicating that user compliance with recommendations can offset the need for manual redistribution and support the current policy of recommending stations within 600 m of the user’s destination. Full article