Search Results (31)

Understanding how selected urban route conditions are associated with e-scooter riders’ visual attention and perceived workload can support human-factor evaluation of micromobility environments. This study examines visual attention and perceived workload during real-world e-scooter riding across three selected urban routes in Zagreb with different infrastructure and traffic-environment characteristics. A field-based experimental methodology was used, integrating eye-tracking with post-ride perceived workload assessment through the National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire. Twenty-eight adults, predominantly novice or occasional e-scooter riders, completed the three selected routes. Visual attention and perceived workload were examined across the selected routes; because only one route represented each route condition, the findings are interpreted as route-level evidence rather than general infrastructure-type effects. One-way repeated-measures analyses of variance (ANOVAs) showed that route condition had a significant effect on mean fixation duration and on the weighted NASA-TLX workload score, indicating that riders’ visual attention and perceived workload varied across the examined routes. To further examine these route-related differences, linear mixed-effects models were used as supporting analyses with participant ID as a random intercept. The results showed that R2 was associated with lower weighted workload than R1, while R3 was associated with higher weighted workload than R1. The route-complexity index was retained as an exploratory route-level descriptor rather than a validated continuous predictor. These findings highlight the importance of considering combined route, infrastructure, and traffic-environment characteristics when planning safer micromobility environments and developing appropriate regulations for e-scooter use. They also provide preliminary route-level human-factor evidence that can support future micromobility route evaluation and the development of human-centred guidance for infrastructure planning and e-scooter regulation. Full article

Cycling places increasingly precise demands on cadence, pedaling angle and rider–bicycle coordination. The purpose of this study is to establish a rider–bicycle coupling model, describe the relationship between the rider and the bicycle, and characterize the predicted activation pattern of lower limb muscles during 60 rpm cadence cycling through the scaled model. A 3D motion capture system was used to record the cycling movements of a healthy male subject with 15 years of cycling experience (26 years old, height 168 cm, weight 56 kg, BMI 19.84 kg per square meter) during a cycling experiment. Pedal force information of vertical load components was acquired using plantar pressure sensors. By combining inverse kinematics and inverse dynamics, the movement trajectories and joint moments of the cycling motion were analyzed, and the activation characteristics of the major lower limb muscle groups were further examined through static optimization. The results showed that under the tested bicycle conditions, the participant model predicted that muscle groups with higher activation levels, including the hamstring, gluteus maximus, and gastrocnemius muscles, exhibited regular periodic changes. The proposed simulation framework provides a methodological reference for biomechanical analysis under the tested 60 rpm cycling condition and may provide a methodological reference for future biomechanical investigations of cycling performance and musculoskeletal function. Full article

Few national comparisons exist on healthcare utilization among e-scooter users, and prior research was limited by the absence of e-scooter-specific ICD-10-CM codes before 2021. Using newly implemented codes, this cross-sectional study compared emergency department disposition and injury patterns between e-scooter riders and bicyclists using the 2021 Nationwide Emergency Department Sample (n = 239,457 weighted encounters). Multivariable logistic regression adjusted for demographic, geographic, and temporal confounders, with interaction terms for insurance status, substance use, and motor vehicle involvement. E-scooter riders were more often female (46.5% vs. 24.7%) and under age 18 (47.6% vs. 37.4%) than bicyclists. Overall adjusted odds of admission or transfer did not differ significantly between groups (OR = 1.03; 95% CI: 0.86–1.23). Motor vehicle involvement significantly modified this relationship, with e-scooter riders showing higher odds of admission (OR = 1.67; 95% CI: 1.12–2.48). No significant effect modification was detected for insurance status or substance use. E-scooter riders more frequently sustained distal extremity fractures consistent with low-energy falls, while bicyclists experienced more proximal fractures and internal trauma. Updated ICD-10-CM codes improve e-scooter injury surveillance, and motor vehicle involvement represents a priority target for mode-specific prevention and infrastructure investment. Full article

Automated recognition of equine–human interaction behaviors from video represents a significant challenge in computational ethology, with critical applications spanning animal welfare assessment, equine-assisted services evaluation, and safety monitoring in equestrian environments. Existing approaches to animal behavior recognition typically focus on single species in isolation, rely solely on facial expression analysis while ignoring full-body posture, or employ flat classification architectures that fail under the severe class imbalances characteristic of naturalistic behavioral datasets. Furthermore, no prior framework integrates simultaneous analysis of both human and equine body language for cross-species interaction classification. This paper presents a novel hierarchical classification framework integrating multi-modal computer vision features to distinguish behavioral states during horse–human encounters. Our methodology employs three complementary feature extraction pipelines: YOLOv8 for spatial relationship modeling, MediaPipe for human postural analysis, and AP-10K for equine body language interpretation. From 28 annotated interaction videos comprising 50,270 temporal samples across five horse breeds, we extract 35 discriminative features capturing proximity dynamics, body orientation, and species-specific behavioral indicators. To address severe class imbalance (18.3:1 ratio between affiliative and avoidant categories), we implement cost-sensitive gradient boosting with automatic class weight optimization within a two-stage hierarchical architecture. The first stage classifies interactions into three parent categories (affiliative, neutral, avoidant) achieving 73.2% balanced accuracy, while stage two discriminates six fine-grained sub-behaviors achieving 88.5% balanced accuracy (under oracle parent-category routing; cascaded end-to-end performance is 62.9% balanced accuracy due to Stage 1 error propagation, identifying parent classification as the primary bottleneck). Notably, our system achieves 85.0% recall on safety-critical avoidant behaviors despite their representation of only 3.8% of the dataset. Extensive ablation studies demonstrate that equine pose features contribute most critically to classification performance, while comprehensive cross-validation analysis confirms model robustness across diverse interaction contexts. The proposed framework establishes the first systematic multimodal cross-species behavioral assessment pipeline in human–animal interaction research, with direct implications for improving equine welfare monitoring and rider safety protocols. Full article

This paper presents an active safety warning system for two-wheeled motorcycles that integrates YOLO v4 image recognition technology with a heuristic weighting mechanism (HWM) model to calculate risk scores and thus alert riders. The system’s analytical core is based on the NVIDIA Jetson TX2 module, with a camera mounted on the left-side rearview mirror of the motorcycle. YOLO is used to identify the type of approaching vehicle and measure the distance between the vehicle and the motorcycle. Moreover, the HWM model takes inputs such as vehicle type, spacing between the motorcycle and the vehicle, motorcycle speed, and distance from the intersection to generate potential risk scores. After training, the YOLO model for vehicle recognition achieved a mean Average Precision (mAP) of 92.78% at an Intersection over Union (IoU) threshold of 0.5. Additionally, the camera mounted at a 30° angle could clearly capture vehicles approaching from the left rear side of the motorcycle, achieving the highest vehicle recognition rate. Moreover, the HWM model generates a reasonable risk score to advise the rider to decelerate when the motorcycle is traveling at high speed with a vehicle approaching from behind, thereby reducing the risk of an accident and enhancing the safety of the motorcyclist. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

This paper considers the vehicle routing optimization problem for O2O takeout delivery services. In the problem, takeout orders are divided into priority orders and ordinary orders based on whether customers have purchased the on-time delivery service. By comprehensively considering the three parties of platform, customers, and riders, a multi-objective function is established to minimize platform costs, maximize average customer satisfaction, and maximize average rider earnings. For the problem, the ε-constraint method and the weighted sum approach are first employed to transform the multi-objective problem into a single-objective one. Then, an improved genetic algorithm integrated with a local search method is designed. The effectiveness of the proposed algorithm is validated by using instances adapted from the existing literature. By setting the lower bounds for average customer satisfaction, as well as the weights for platform costs and average rider earnings, it can provide flexible and effective decision-making references for decision makers. Full article

Unmanned aerial vehicles (UAVs) can be utilized as airborne base stations to deliver wireless communication and federated learning (FL) training services for ground vehicles. However, most existing studies assume that vehicles (clients) and UAVs (model owners) offer services voluntarily. In reality, participants (FL clients and model owners) are selfish and will not engage in training without compensation. Meanwhile, due to the heterogeneity of participants and the presence of free-riders and Byzantine behaviors, the quality of vehicles’ model updates can vary significantly. To incentivize participants to engage in model training and ensure reliable outcomes, this paper designs a reliable incentive mechanism (FedBeam) based on game theory. Specifically, we model the cooperation problem between model owners and clients as a two-layer Stackelberg game and prove the existence and uniqueness of the Stackelberg equilibrium (SE). For the cooperation among model owners, we formulate the problem as a coalition game and based on this, analyze and design a coalition formation algorithm to derive the Pareto optimal social utility. Additionally, to achieve reliable FL model updates, we design a weighted-beta (Wbeta) reputation update mechanism to incentivize FL clients to provide high-quality model updates. The experimental results show that compared to the baselines, the proposed incentive mechanism improves social welfare by 17.6% and test accuracy by 5.5% on simulated and real datasets, respectively. Full article

Electric vehicle accidents on the road occur frequently, and head injuries are often the cause of serious casualties. However, most electric vehicle riders seldom wear helmets. Therefore, combining target detection algorithms with road cameras to intelligently monitor helmet-wearing has extremely important research significance. Therefore, a helmet-wearing detection algorithm based on the improved YOLOv8n model, PRE-YOLO, is proposed. First, we add small target detection layers and prune large target detection layers. The sophisticated algorithm considerably boosts the effectiveness of data manipulation while significantly reducing model parameters and size. Secondly, we introduce a convolutional module that integrates receptive field attention convolution and CA mechanisms into the backbone network, enhancing feature extraction capabilities by enhancing attention weights within both channel and spatial aspects. Lastly, we incorporate an EMA mechanism into the C2f module, which strengthens feature perception and captures more characteristic information while maintaining the same model parameter size. The experimental outcomes indicate that in comparison to the original model, the proposed PRE-YOLO model in this paper has improved by 1.3%, 1.7%, 2.2%, and 2.6% in terms of precision P, recall R, mAP@0.5, and mAP@0.5:0.95, respectively. At the same time, the number of model parameters has been reduced by 33.3%, and the model size has been reduced by 1.8 MB. Generalization experiments are conducted on the TWHD and EBHD datasets to further verify the versatility of the model. The research findings provide solutions for further improving the accuracy and efficiency of helmet-wearing detection on complex traffic roads, offering references for enhancing safety and intelligence in traffic. Full article

Weight-related issues can be prevalent in elite-level sports, especially in men’s road cycling, where riders may exhibit harmful behaviours, with potentially adverse outcomes for mental and physical health. This study investigated Body Mass Index (BMI) values amongst the top five finishers in the three Grand Tours and the five Monuments races between 1994 and 2023 to assess longitudinal patterns. Publicly available height and weight figures were sourced from ProCyclingStats and BMI scores were calculated for n = 154 and n = 255 individual athletes for the Grand Tours and Monuments, respectively. Two analyses were conducted with correlations and ANOVAs: the first included the BMIs of all top-five finishes and the second focussed on the BMIs of new top-five entrants. The results from both analyses revealed consistent mean BMI decreases over the years and larger effect sizes were apparent in the Grand Tours compared to the Monuments. Although lower BMIs are associated with certain performance advantages, these declining trajectories suggest a need for enhanced awareness in the cycling community and possible regulatory measures and educational programmes to promote the sustainable wellbeing of riders. This may be particularly pertinent given the wider evidence of unhealthy weight-related attitudes and behaviours throughout the sport. Full article

Jumps racing is a form of Thoroughbred horse racing that involves hurdles and steeples and typically longer distances, and heavier weights compared with flat racing, which does not incorporate obstacles. In Australia, jumps racing is carried out only in Victoria, one of eight states and territories. The continuation of jumps racing is contentious due to the higher risk of fatalities, falls and injuries for horses, compared with flat racing. While measures have been introduced by the industry to improve the safety of riders and horses, the rates of fatalities, falls and injuries in horses participating in jumps races have not been collectively reported in Australia since the 2012 to 2014 race seasons. Although information on individual horse fatalities, falls and injuries is published by Racing Victoria in Stewards’ Reports, the data are not aggregated, and so cannot readily be used to assess trends or evaluate the efficacy of safety measures introduced by the industry. The aim of this study was to determine the fatality, fall and injury rates for horses participating in hurdle and steeplechase races in Victoria in the 2022 and 2023 Thoroughbred horse jumps racing seasons compared with horses participating in flat races at the same race meets. Data on horse fatalities, falls and injuries were extracted from the published Racing Victoria race results and Stewards’ Reports for the jumps races (n = 150) and corresponding flat races (n = 157) held at the 38 jumps race meets in Victoria in 2022 and 2023. Overall, horse fatalities, falls and injuries occurred at higher rates in jumps races compared with flat races during the study period. The rate of horse fatalities in jumps races was 3.3 per 1000 starts, with no fatalities in flat races. The rate of horse falls in hurdle races was 24 per 1000 starts and 41.6 per 1000 starts in steeplechase races, comparable with rates previously reported in the 2012 to 2014 seasons. There were no falls in flat races. Horse injuries occurred at a rate of 68.9 per 1000 starts in jumps races compared with 18.8 per 1000 starts in flat races. In hurdle and steeplechase races, veterinary clearance being required following horse injury was 5.4 times (OR 5.4, 95% CI 2.8–10.2) and 7.2 times (OR 7.2, 95% CI 3.3–15.6) more likely, respectively, compared with flat races. The risk of trauma was 4 times more likely in hurdle and steeplechase races (OR 4.8, 95% CI 1.7–13.3 and OR 4.1, 95% CI 1.2–13.4, respectively) and the risk of lameness was increased by 2.5 times in hurdles (OR 2.5, 95% CI 1.2–5.2) and 5.1 times in steeplechase races (OR 5.1, 95% CI 2.3–11.5), compared with flat races. These findings support concerns about the welfare of horses involved in jumps racing and of the need for further safety measures to reduce these risks. Full article

Agonistic behaviours are often directed at other animals for self-defence or to increase distance from valued resources, such as food. Examples include aggression and counter-predator behaviours. Contemporary diets may boost the value of food as a resource and create unanticipated associations with the humans who deliver it. At the same time the domestic horse is asked to carry the weight of riders and perform manoeuvres that, ethologically, are out-of-context and may be associated with instances of pain, confusion, or fear. Agonistic responses can endanger personnel and conspecifics. They are traditionally grouped along with so-called vices as being undesirable and worthy of punishment; a response that can often make horses more dangerous. The current study used data from the validated online Equine Behavioural and Research Questionnaire (E-BARQ) to explore the agonistic behaviours (as reported by the owners) of 2734 horses. With a focus on ridden horses, the behaviours of interest in the current study ranged from biting and bite threats and kicking and kick threats to tail swishing as an accompaniment to signs of escalating irritation when horses are approached, prepared for ridden work, ridden, and hosed down (e.g., after work). Analysis of the responses according to the context in which they arise included a dendrographic analysis that identified five clusters of agonistic behaviours among certain groups of horses and a principal component analysis that revealed six components, strongly related to the five clusters. Taken together, these results highlight the prospect that the motivation to show these responses differs with context. The clusters with common characteristics were those observed in the context of: locomotion under saddle; saddling; reactions in a familiar environment, inter-specific threats, and intra-specific threats. These findings highlight the potential roles of fear and pain in such unwelcome responses and challenge the simplistic view that the problems lie with the nature of the horses themselves rather than historic or current management practices. Improved understanding of agonistic responses in horses will reduce the inclination of owners to label horses that show such context-specific responses as being generally aggressive. Full article

Mountain bikes with electric assistance (e-bikes) have gained popularity recently by allowing riders to increase their pedaling power through an electric motor. This innovation has raised questions about how e-bikes compare to traditional mountain bikes regarding physical effort, speed, and physiological demands. By examining these factors, the study aims to compare and characterize differences in performance-related parameters when using an electric-assisted mountain bike compared to a conventional mountain bike on different types of terrain (uphill, downhill, flat section, technically demanding terrain) concerning power output, velocity, cardiorespiratory parameters, and energy expenditure. Six experienced mountain bikers (mean age: 44.6 ± 6.4 years, mean body height: 173.3 ± 5.6 cm, mean body weight: 70.6 ± 4.9 kg) cycled 4.5 km on varying off-road terrain at their own race pace, once with and once without electrical assistance, in randomized order. The results of the study indicate significantly faster (24.3 ± 1.85 to 17.2 ± 1.22 km/h (p < 0.001)) cycling on an electric-assisted mountain bike, which reduces cardiorespiratory parameters and metabolic effort as well as results in less demanding workload (138.5 ± 31.8 W) during the cycling with an electric-assisted mountain bike in comparison to a conventional mountain bike (217.5 ± 24.3 W (p < 0.001)). The results indicate significant differences especially when riding uphill. The performance advantage of an electrically assisted mountain bike diminishes compared to a conventional mountain bike on downhill, flat, or technically challenging terrain. The highlighted advantages of electric-assisted mountain bikes could represent a novel strategy for cycling in different terrains to optimize efficiency. Full article

Currently, studies regarding the factors influencing the real-world electricity consumption of electric motorcycles are lacking. The objective of this study was to examine the factors influencing the real-world electricity consumption of electric motorcycles when driving along an uncongested road network. This study developed an onboard measurement device to collect on-road data, including instant speed data and electricity consumption, from the test electric motorcycle while it was driving on a real-world road. Overall, 105 participants (n = 105) drove the test motorcycle along the uncongested urban road network. Multiple linear regression analysis was applied to explore the effect of influencing variables on the electricity consumption of electric motorcycles. The analysis results revealed that the rider’s weight and average running speed positively influenced electricity consumption, whereas decelerating time negatively influenced electricity consumption. Noticeably, the rider’s weight affected electricity consumption more than other factors. The lightweighting of electric motorcycles was mainly recommended to lower electricity consumption. Subsequently, CO₂ emissions from electricity generation could be reduced. Full article

The research objective is to approach the dynamic and consumed electrical energy of an electric scooter by varying the key input parameters, including rider mass, electric scooter mass, wind speed, wheel radius, and slope grade. A simulation model of an electric scooter was applied in a MATLAB-Simulink environment to investigate the scooter velocity, required power, battery voltage, and propulsion torque of the e-scooter. It was established by employing mathematical equations during the of electric scooters. The study found that the scooter velocity and electricity consumption were optimized by 3.9% and 0.08%, respectively, when the scooter weight decreased from 26 to 10 kg. The scooter velocity, electricity consumption, and required power decreased by 23.2%, 0.55%, and 8.56%, respectively, when the slope grade decreased from 1.15% to 0%. Following a wind speed reduction from 4 to 0 m/s, the consumed electricity and required power were optimized by 0.2% and 5.5%, respectively. The consumed electricity increased by 0.2% and the scooter velocity and required power significantly increased by 36.5% and 34.3% when the wheel radius increased from 0.105 to 0.185 m. Furthermore, the e-scooter could achieve an effective performance with a weight of 10 kg, wheel radius of 0.185 m, wind speed of 0 km/h, slope grade of 0%, and minimal rider weight. The simulation results showed that the scooter’s effective performance range and consumed electrical energy could be optimized by suitably adjusting the key structures and operating parameters. To support this research, a concurrent experiment investigated the dynamic characteristics and electricity consumption of the electric scooter during operation. The experimental and simulated results had the same patterns in similar initial conditions. Full article