Search Results (131)

Electric-assist bicycles have recently become very popular. However, riding them generally requires significantly more energy, generated simultaneously by the motor and the rider, compared to much lighter traditional bicycles. Assessing the energy efficiency of electric-assist bicycles in comparison to traditional bikes allows us to determine in which cases using electric bikes is cost-effective and in which it is not. This study proposes a method for evaluating the energy efficiency of bicycles, which stands out by relying on relatively imprecise data recorded at low frequency by a commercial bike computer with accessories. The core of the method is an algorithm developed by the authors to determine the tractive force acting on the bicycle and rider, based on a minimal set of recorded data: road incline, riding speed, and the wind speed component parallel to the direction of movement. Depending on the situation, the tractive force may act as a driving force or as a braking force. Based on the calculated tractive force, the power required to maintain the recorded bicycle speed can be estimated. Full article

Bicycle riding requires a high standard width and continuity of lanes, as an appropriate width directly improves the service level of the lanes. Therefore, the width of bicycle lanes should be designed considering the characteristics of the bicycle traffic flow and the actual conditions of an area. In order to explore the relationship between bicycle traffic flow characteristics and lane width, this study references the vehicle traffic flow model and introduces the concept of bicycle traffic flow width, defined as the average width of bicycle traffic flow over a certain distance in a unit of time. Based on measured data, this study analyzes the relationships among bicycle traffic flow, lane width, and other parameters. The research results show that when the bicycle lane width is between 2.0 and 3.4 m, there is a clear linear relationship between the speed of bicycle traffic flow and the traffic flow width, with bicycle traffic flow width increasing as speed increases. Furthermore, overly wide bicycle lanes can result in more instances of bicycle over-speeding. These findings will guide the design of bicycle lanes. Full article

In response to the lack of ability to handle multidimensional data in current research methods for shared bicycle travel patterns, and the fact that correlation analysis is only conducted on a single feature, this study investigates the travel pattern using tensor decomposition and a random forest model. Based on the riding data of dockless shared bicycles in Shenzhen, tensor decomposition is applied to extract three shared bicycle travel patterns: peak-high traffic pattern, steady traffic pattern, and off-peak high traffic pattern. Spatially, each pattern exhibits clustering, and the travel volume decreases from the center to the periphery. Based on this, with 13 built environment factors as feature variables, a random forest model is trained. Importance and interaction analyses are performed for both individual features and feature combinations. The results indicate that the random forest model demonstrates excellent fitting performance and accuracy. Furthermore, for the peak-high traffic pattern, the combination of the length of primary roads and the number of companies contributes the most, while for the steady traffic pattern, it is the combination of the number of malls and companies. Finally, for the off-peak high traffic pattern, the influence of the number of malls and interests is the most significant. Full article

Riding comfort and safety are essential requirements for any form of transportation but particularly for electric bicycles (e-bikes), which are highly affected by varying road conditions. These factors largely depend on the effectiveness of the e-bike’s control strategy. While several studies have proposed control approaches that address comfort and safety, vibration—an influential factor in both structural integrity and rider experience—has received limited attention during the design phase. Moreover, many commercially available e-bikes provide manual assistance-level settings, leaving comfort and safety management to the rider’s experience. This study proposes a Road-Adaptive Vibration Reduction System (RAVRS) that can be deployed on an e-bike rider’s smartphone to automatically maintain riding comfort and safety using manual assistance control. A fuzzy-based control algorithm is adopted to dynamically select the appropriate assistance level, aiming to minimize vibration while maintaining velocity and acceleration within thresholds associated with comfort and safety. This study presents a vibration analysis to highlight the significance of vibration control in improving electronic reliability, reducing mechanical fatigue, and enhancing user experience. A functional prototype of the RAVRS was implemented and evaluated using real-world data collected from experimental trips. The simulation results demonstrate that the proposed system achieves effective control of speed and acceleration, with success rates of 83.97% and 99.79%, respectively, outperforming existing control strategies. In addition, the proposed RAVRS significantly enhances the riding experience by improving both comfort and safety. 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

Micromobility is a current and growing integrated transport mode that has its own regulations and requirements for riding. In this context, bicycle usage has been regulated years before. However, the coexistence with new micromobility vehicles, such as e-scooters, has generated the necessity of the development of additional regulatory frameworks. Administrators and technicians have been working for the last 7 years on this aspect. However, a proper evaluation from the user perspective has not been carried out. Thus, there is a need to identify whether micromobility users are aware of the regulations that they must comply with. This research has analyzed the users’ knowledge through a survey, a transport-typical data collection method, but used implicitly as an evaluation, where a score was obtained per user. As a result, the average score obtained was 4.5, reflecting an insufficient qualification. Additionally, statistically significant differences were found between the average score obtained between cyclists and personal mobility vehicle (PMV) users, as well as mean differences between age, micromobility vehicle, ownership, and holding a driver’s license. In conclusion, a new gap has been found in relation to micromobility users’ behavior that has to be addressed. Full article

Micromobility vehicles, e-scooters and e–bicycles in particular, gain an increasing popularity but also receive criticism, mainly due to road safety issues and their carbon footprint, particularly in relation to their Li-ion batteries. Available field data are not sufficient to explore those issues. Important input variables, such as riders’ reaction time, the impact of human factors on riders’ safety, battery performance degradation with time, remain unknown. This paper presents the design, development, initial calibration and validation of two novel driving simulators, one for an e-scooter and one, for an e-bicycle. The simulators are already operational and used to acquire new knowledge on driving behavior and battery performance. By enabling a better understanding of e-vehicle performance and safety, these simulators contribute to reducing the environmental impact of micromobility by optimizing battery usage and improving vehicle design for sustainability. The paper describes the overall configuration and the main technical specifications of both simulators and provides a thorough description of all their mechanical and electromechanical components. It documents the initial calibration process before launching the experiments and presents the validation methodology with the participation of over 100 users. The outcomes of future experiments are expected to be beneficial to (i) researchers who will gain new insights on e-vehicle performance, (ii) users, enabling them to make informed decisions on vehicle choice and riding patterns, (iii) urban planners on improving urban infrastructure design, (iv) vehicle manufacturers on identifying customer needs and enhancing vehicle design for sustainability, and (v) Public Authorities on adjusting vehicle and infrastructure specifications to reduce the carbon footprint of urban mobility. Full article

In smart cities, bicycle-sharing systems have become essential as last-mile transportation solutions, seamlessly integrating into urban mobility networks worldwide. To improve riding efficiency, the development of automatic gear-shifting systems for electric bicycles has gained significant attention. This study presents a novel fuzzy logic controller (FLC) designed to address the challenges of frequent and unstable gear shifts in automatic bicycle transmissions. Unlike traditional systems that rely solely on velocity or cadence as inputs, the proposed FLC incorporates both acceleration and slope data to enhance shifting stability and cadence regulation. By replacing velocity with acceleration and integrating slope information, the system minimizes frequent shifting and improves overall performance. Experimental and simulation results demonstrate that the proposed approach reduces acceleration ripple, stabilizes gear-shifting, and maintains cadence within the desired range, ensuring a smoother and more comfortable riding experience. The proposed approach significantly reduces acceleration ripple by 1 m/s², maintains target cadence, and aligns gear shifts with design intent, yielding a substantial 20% safety improvement. These advancements offer particular promise for public bicycle-sharing systems, providing a robust and adaptable solution suited to diverse cycling conditions and rider profiles. Full article

Ensuring comfort in light mobility is a crucial aspect for supporting individuals’ well-being and safety while driving scooters, riding bicycles, etc. In fact, factors such as the hand grip on the handlebar, positions of the wrist and arm, overall body posture, and affecting vibrations play key roles. Wearable systems offer the ability to noninvasively monitor physiological parameters, such as body temperature and heart rate, aiding in personalized comfort assessment. In this context, user positions while driving or riding are, on the other hand, more challenging to monitor ecologically. Developing effective smart gloves as a support for comfort and movement monitoring introduces technical complexities, particularly in sensor selection and integration. Light and flexible sensors can help in this regard by ensuring reliable sensing and thus addressing the optimization of the comfort for the driver. In this work, a novel wireless smart glove is proposed, integrating four bend sensors, four force-sensitive sensors, and one inertial measurement unit for measuring the finger movements, hand orientation, and the contact force exerted by the hand while grasping the handlebar during driving or riding. The smart glove has been proven to be repeatable (1.7%) and effective, distinguishing between different grasped objects, such as a flask, a handlebar, a tennis ball, and a small box. Additionally, it proved to be a valuable tool for monitoring specific actions while riding bicycles, such as braking, and for optimizing the posture during the ride. Full article

The traffic behavior characteristics within university campuses have received limited scholarly attention, despite their distinct differences from external road networks. These differences include the predominance of non-motorized vehicles and pedestrians in traffic flow composition, as well as traffic peaks primarily coinciding with class transition periods. To investigate the riding behavior of cyclists on university campuses, this study examines cyclist attention, proposes a novel method for constructing a rider attention recognition framework, utilizes a hierarchical ordered logistic model to analyze the factors influencing attention, and evaluates the model’s performance. The findings reveal that traffic density and riding style significantly influence cyclists’ eye-tracking characteristics, which serve as indicators of their attention levels. The covariates of lane gaze time and the coefficient of variation in pupil diameter exhibited significant effects, indicating that a hierarchical ordered logistic model incorporating these covariates can more effectively capture the impact of influencing factors on cyclist attention. Moreover, the hierarchical ordered logistic model achieved a 7.22% improvement in predictive performance compared to the standard ordered logistic model. Additionally, cyclists exhibiting a “conservative” riding style were found to be more attentive than those adopting a “aggressive” riding style. Similarly, cyclists navigating “sparse” traffic conditions were more likely to maintain attention compared to those in “dense” traffic scenarios. These findings provide valuable insights into the riding behavior of university campus cyclists and have significant implications for improving traffic safety within such environments. 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

Cycling is now a very popular sport and leisure activity or commuting tool around the world, with its popularity growing especially during the epidemic. The traditional bicycle depends on a chain driving mechanism to move forward (This paper is an extended version of our paper published in The 16th Intelligent Living Technology Conference, Taichung, Taiwan, 2 June 2022). However, its transmission chain is easily dirtied and loosened so that regular maintenance is highly demanded to sustain normal function. To achieve the idea of maintenance-free, self-calibrating, and efficient mechanism operation, a wireless-controlled gear shifter for shaft driven bicycles is proposed, not only to overcome the limitations of the traditional chain driving mechanism, but also to make riding control more convenient. Firstly, an actuated gear shifter module coordinated with the gear positioning system was designed. Secondly, a remote controller module with information organic light-emitting diodes (OLEDs) and shift operation buttons was developed. Both modules use independent batteries and a Bluetooth Low Energy (BLE) interface to communicate with each other for wireless shifting control. The experimental results verify the effectiveness of the proposed system in terms of accuracy, rapidness, and robustness. Full article

The rapid spread of micromobility vehicles such as bicycles and electric scooters poses new challenges to urban transportation systems, particularly in terms of road safety and infrastructure integration. This study investigates the driving behavior of micromobility users at a mini-roundabout, focusing on their speed profiles and their position within the lane during the entry, circulation, and exit phases. A structured recruitment process was used to select 20 participants with previous micromobility experience. Participants performed crossing maneuvers at a mini-roundabout in Gravina di Catania, Italy, which were monitored using drone footage and analyzed with tracking software to extract trajectories and speed data. The results show significant differences between e-scooter and bicycle users, with bicycles showing less speed variability, especially during the crossing and exit phases, while e-scooters showed greater variability, especially during the entry and exit phases. The results highlight the influence of vehicle stability and user posture on riding behavior and emphasize the need for infrastructure adaptations to increase safety. Mini-roundabouts designed for moderate speed are identified as a promising solution to improve the coexistence of micromobility and motor vehicles. This research identifies key differences in speed profiles and behavioral patterns between e-scooter and bicycle users, offering actionable insights and recommendations for safer and more efficient urban infrastructure. These contributions provide valuable guidance for urban planners and policymakers in promoting safer and more sustainable urban mobility. 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

The emergence of micro-mobility transportation in urban areas has led to a transformative shift in mobility options, yet it has also brought about heightened traffic conflicts and crashes. This research addresses these challenges by pioneering the integration of image-processing techniques with machine learning methodologies to analyze crash diagrams. The study aims to extract latent features from crash data, specifically focusing on understanding the factors influencing injury severity among vehicle and micro-mobility crashes in Michigan’s urban areas. Micro-mobility devices analyzed in this study are bicycles, e-wheelchairs, skateboards, and e-scooters. The AlexNet Convolutional Neural Network (CNN) was utilized to identify various attributes from crash diagrams, enabling the recognition and classification of micro-mobility device collision locations into three categories: roadside, shoulder, and bicycle lane. This study utilized the 2023 Michigan UD-10 crash reports comprising 1174 diverse micro-mobility crash diagrams. Subsequently, the Random Forest classification algorithm was utilized to pinpoint the primary factors and their interactions that affect the severity of micro-mobility injuries. The results suggest that roads with speed limits exceeding 40 mph are the most significant factor in determining the severity of micro-mobility injuries. In addition, micro-mobility rider violations and motorists left-turning maneuvers are associated with more severe crash outcomes. In addition, the findings emphasize the overall effect of many different variables, such as improper lane use, violations, and hazardous actions by micro-mobility users. These factors demonstrate elevated rates of prevalence among younger micro-mobility users and are found to be associated with distracted motorists, elderly motorists, or those who ride during nighttime. Full article