Search Results (50)

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

Purposes: This study aims to construct a research framework integrating the theory of human values and Self-Determination Theory (SDT) to examine whether parents’ sport values influence their support for children’s continued participation in balance bike activities in terms of the mediation of participation motivation. Methods: Data were collected from 439 parents whose children participated in balance bike activities using a snowball sampling method. Descriptive statistics and structural equation modeling (SEM) were employed to analyze the relationships among parents’ sport values, participation motivation, and continued participation intention. Results: The findings revealed that parents’ sport values significantly predicted participation motivation, which, in turn, remarkably influenced continued participation intention. Participation motivation fully mediated the relationship between sport values and continued participation intention, supporting SDT’s assumption of motivational internalization and highlighting the crucial role of intrinsic motivation. Full article

The increasing popularity of e-biking and e-mountain biking offers new opportunities for sustainable tourism and environmental education, particularly in mountain regions. This study focuses on the Italy–Switzerland “E-Bike” project, which integrates e-bike-friendly routes with scientific and cultural education across the Alps. By analyzing key points of interest along the routes, particularly glaciers and earth pyramids in Lombardy, we explore strategies for sustainable management, conservation, and public engagement. Glaciers (Forni and Ventina), facing rapid retreat due to climate change, represent sensitive environments requiring monitoring and visitor regulation. Similarly, earth pyramids in Postalesio exemplify fragile landforms shaped by erosion, requiring visitor management. This study highlights the need for strategic promotion, clear scientific communication, and sustainable tourism practices to balance conservation with accessibility. E-biking facilitates low-impact exploration of geosites, enhancing public awareness of environmental challenges while minimizing ecological footprints. Innovative digital tools (QR-coded virtual guides) enhance visitor education and engagement. By integrating e-bike tourism with geoheritage conservation, this study proposes guidelines for managing soft mobility in mountain areas, combining conservation needs with accessibility, and fostering public engagement. These findings contribute to broader discussions on sustainable tourism development, offering a replicable model for other regions seeking to harmonize recreation with environmental stewardship. 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

Modern and smart cities prioritize providing sufficient facilities for inclusive and bicycle-friendly streets. Several methods have been developed to assess city bicycle environments at street, neighborhood, and city levels. However, the importance of micro-level indicators and bicyclists’ perceptions cannot be neglected when developing a bikeability index (BI). Therefore, this paper proposes a new BI method for evaluating and providing suggestions for improving city streets, focusing on bicycle infrastructure facilities. The proposed BI is an analytical system aggregating multiple bikeability indicators into a structured index using weighed coefficients and scores. In addition, the study introduces bicycle infrastructure indicators using five bicycle design principles acknowledged in the literature, experts, and city authorities worldwide. A questionnaire was used to collect data from cyclists to find the weights and scores of the indicators. The survey of 383 participants showed a balanced gender distribution and a predominantly younger population, with most respondents holding bachelor’s or master’s degrees and 57.4% being students. Most participants travel 2–5 km per day and cycle 3 to 5 days per week. Among the criteria, respondents graded safety as the most important, followed by comfort on bicycle paths. Confirmatory factor analysis (CFA) is used to estimate weights of the bikeability indicators, with the values of the resultant factor loadings used as their weights. The highest-weight indicator was the presence of bicycle infrastructure (0.753), while the lowest-weight indicator was slope (0.302). The proposed BI was applied to various bike lanes and streets in Hasselt, Belgium. The developed BI is a useful tool for urban planners to identify existing problems in bicycle streets and provide potential improvements. Full article

Background/Objectives: Empowering our children and youth to cycle empowers them to pursue a healthier, fuller, and more responsible life. The present study implemented the Learning to Cycle program with the following aims: (i) to promote learning to cycle; (ii) to investigate and compare the use of different learning bicycles, i.e., balance bicycle (BB) and bicycle with training wheels (BTW); (iii) to investigate the influence of body composition during this learning process. Methods: The program was implemented through a quasi-experimental study involving two intervention groups, with pre- and post-test evaluations. The program was applied to 50 children (M = 5.82 ± 0.94 years, 23 girls) who did not know how to cycle previously. One group explored the BB and the other the BTW for six sessions, followed by four more sessions with the conventional bicycle (CB) for both groups. The assessment of independent cycling was considered as the ability to perform, sequentially and unaided, and the various cycling milestones: self-launch, ride, and brake. The children’s body composition was accessed by the BMI’s percentile and classification according to their age and sex. Results: The program had a success rate of 88.24% for acquiring independent cycling, with 100% success in the BB group and 76.92% in the BTW group. The BB children learned significantly faster to self-launch, ride, brake, and cycle independently. Children with higher BMI percentiles faced greater challenges in achieving balance milestones. Conclusions: BB are recommended, especially for overweight and obese children, as they help develop balance from the onset, and showed to be more efficient in learning to cycle than the BTW. Full article

Enabled by recent technological advances and the substantial growth of the sharing economy, electric bike-sharing (EBS) has experienced rapid growth in medium-sized Chinese cities, yet its impact on for-hire vehicle (FHV) services remains insufficiently studied. Using a six-month longitudinal dataset from Yancheng, a representative medium-sized city in China, we employ an instrumental variable method to address potential endogeneity and provide quantitative empirical analysis. The analysis identifies a significant substitution effect, where a 1% increase in EBS trips corresponds to a 0.810% decline in FHV ridership. Through heterogeneity analyses, this study reveals that the substitutive effect of EBS is stronger in central downtown, which has denser infrastructure, while its impact diminishes in peripheral districts. Furthermore, unfavorable weather conditions mitigate the substitutive effect, as users increasingly rely on FHVs for their reliability and comfort during unfavorable conditions. The findings of this study highlight the necessity of integrating EBS into the electrified shared mobility ecosystem in a balanced manner to prevent disruptions to the existing transportation network and provide valuable guidance for sustainable and stable transportation planning in medium-sized cities and similar urban contexts. Full article

Background/Objectives: Riding a bicycle is a foundational movement skill that can be acquired at an early age. The most common training bicycle has lateral training wheels (BTW). However, the balance bike (BB) has consistently been regarded as more efficient, as children require less time on this bike to successfully transition to a traditional bike (TB). The reasons for this greater efficiency remain unclear, but it is hypothesized that it is due to the immediate balancing requirements for learners. This study aimed to investigate the reasons why the BB is more efficient than the BTW for learning to cycle on a TB. Methods: We compared the variability of the child–bicycle system throughout the learning process with these two types of training bicycles and after transitioning to the TB. Data were collected during the Learning to Cycle Program, with 23 children (6.00 ± 1.2 years old) included. Participants were divided into two experimental training groups, BB (N = 12) and BTW (N = 11). The angular velocity data of the child–bicycle system were collected by four inertial measurement sensors (IMUs), located on the child’s vertex and T2 and the bicycle frame and handlebar, in three time phases: (i) before practice sessions, (ii) immediately after practice sessions, and (iii), two months after practice sessions with the TB. The largest Lyapunov exponents were calculated to assess movement variability. Conclusions: Results supported the hypothesis that the BB affords greater functional variability during practice sessions compared to the BTW, affording more functionally adaptive responses in the learning transition to using a TB. Full article

Bikes and bike networks are for younger fit bicyclists, and the U.S. continues to not serve older individuals, with and without dementia, and caregivers. Biking is a sustainable form of transportation, and expanding the biking population would address climate change while improving health. To our knowledge, research has not been conducted in which seniors indicate their preferences for bike styles and networks, health concerns, and desires to bike. Conducted in four senior-living communities in New England, the Visual and Verbal Preference Survey involved 178 participants (female—50%/male—29.8%; age 20–85 52.8%; age > 85 41.6% with 19.6% missing gender and 5.6% missing age). Bike test riding in two senior communities involved 50 participants (female—50%/male—40%; age 66–75 14%; 76–85 60%; age 86–95 16% with 10% missing gender and age). Seniors preferred the adult tricycle, followed by the three- and four-wheeled two-seated bikes, and to bicycle for 30 min 2 days a week, have a bathroom break every hour, and have a bicycle loop. Balance and fear of falling were major concerns, but they wanted to bicycle with family, children, and grandchildren. In two of the communities, seniors test rode three senior-friendly Van Raam bikes. The results mirrored the pre-test survey responses, where the adult tricycle (Easy Rider) was most preferred, followed by the three-wheeled two-seater bike (Fun2Go) and the low-step regular bike (Balance Bike). One community purchased the Fun2Go. Full article

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework’s viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York’s Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers. Full article

Known for their compact size, mobility, and off-road capabilities, motorcycles are increasingly used for logistics, emergency rescue, and reconnaissance. However, due to their two-wheeled nature, motorcycles are susceptible to instability, heightening the risk of tipping during cornering. This study includes some research and exploration into the following aspects: (1) The design of a front-wheel steering self-balancing controller. It achieves self-balance during motion by adjusting the front-wheel steering angle through manipulation of handlebar torque. (2) Trajectory tracking control based on preview control theory. It establishes a proportional relationship between lateral deviation and lean angle, as determined by path preview. The desired lean angle then serves as input for the self-balancing controller. (3) A pre-braking controller for enhanced active safety. To prevent lateral slide on wet and slippery surfaces, the controller is designed considering the motorcycle’s maximum braking deceleration. These advancements were validated via a joint BikeSim and Matlab/Simulink simulation, which included scenarios such as double lane changes and 60 m-radius turns. The results demonstrate that the intelligent motorcycle equipped with the proposed control algorithm tracks trajectories and maintains stability effectively. Full article

In this study, we present a framework for optimizing pick-up and delivery operations in bike-sharing systems (BSSs), with particular emphasis on inventory rebalancing and vehicle routing to enhance operational efficiency. By employing a hybrid genetic algorithm (HGA), this study integrates sophisticated predictive models with multi-objective optimization techniques to strike a balance between operational efficiency and demand fulfillment in urban bike-share networks. For probabilistic demand forecasting, the DeepAR model is applied to a large number of bike stations clustered by geological proximity to enable stochastic inventory management. Our proposed HGA approach leverages both the genetic algorithm for generating feasible vehicle routes and mixed-integer programming for bike rebalancing to minimize travel distances while maintaining balanced inventory levels across all clustered stations. Through rigorous empirical evaluations, we demonstrate the effectiveness of our proposed methodology in improving service quality, thus making significant contributions to sustainable urban mobility. This study not only pushes the boundaries of theoretical knowledge in BSS logistics optimization but also offers managerial insights for practical implementation, particularly in densely populated urban settings. Full article

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking. Full article

Electric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and others in parallel within custom architectures. They need to be controlled against over current, temperature, inner pressure and voltage, and their charge/discharge needs to be continuously monitored and balanced among the cells. Such a battery management system exhibits embarrassingly parallel computing, as hundreds of cells offer the opportunity for scalable and decentralized monitoring and control. In recent years, tiny machine learning has emerged as a data-driven black-box approach to address application problems at the edge by using very limited energy, computational and storage resources to achieve under mW power consumption. Examples of tiny devices at the edge include microcontrollers capable of 10–100 s MHz with 100 s KiB to few MB embedded memory. This study addressed battery management systems with a particular focus on state-of-charge prediction. Several machine learning workloads were studied by using IEEE open-source datasets to profile their accuracy. Moreover, their deployability on a range of microcontrollers was studied, and their memory footprints were reported in a very detailed manner. Finally, computational requirements were proposed with respect to the parallel nature of the battery system architecture, suggesting a per cell and per module tiny, decentralized artificial intelligence system architecture. Full article

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone’s GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model’s ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model’s capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin–destination (OD) matrix to better understand how people move within the city. Full article