Small Fleet, Big Impact: Enhancing Shared Micromobility Efficiency through Minimal Autonomous Vehicle Deployment

Environmental and Economic Prospects of Low-Carbon Vehicles in Support of European Commission 2030 City Logistics Fleet Goals

Sustainable development means taking care of the environment, which also means promoting green transport, which involves the systematic development of personal transport in its broadest sense. The positive aspects associated with cheap and convenient electric transport are intertwined with the problem of collisions and accidents. While developing road infrastructure for electric vehicles such as scooters, bicycles, and others, research should be conducted in parallel to ensure the highest possible level of safety for users. There is also an increase in the number of people using bicycles and electric scooters, which develop significant speeds. The problem of accidents among users of classic and electric bicycles and scooters is evident, and post-accident injuries pose a serious challenge to medical practitioners. The literature is rich in statistical analyses of accidents among users of scooters and bicycles, but there are no studies where the behaviour of users of bicycles, scooters, etc. is analysed. The authors of this study set out to develop a measurement system to assess the traffic safety of people using bicycles and scooters. The device uses LIDAR to record the speed of the vehicle and a camera, the images of which are processed by an algorithm in order to classify the user as being on a bicycle or scooter and using or not using head protection with a helmet. It is also possible to analyse the behaviour of the vehicle users under study. The article describes the built measurement device and presents the results of the initial measurements made by the device.

Background:Use of electric bikes (EBs) and electric scooters (ESs) has increased dramatically, and so are EB-related and ES-related orthopaedic injuries. There is limited research regarding the severity of EB injuries and whether they more closely resemble ES, motorcycle (MC), or pedal bicycle (PB) related injuries. The aim of this study was to characterize injury patterns associated with EB accidents and compare injury severity and details with those of PB, ES, and MC accidents.Methods:This was a retrospective review of trauma patients presenting to an academic, level one trauma center between 2019 and 2022 to compare EB, ES, PB, and MC trauma patients. Descriptive statistics were obtained to characterize demographic data. Two-sided Fisher exact tests were used to compare categorical data, and ANOVA was used to compare continuous variables between groups.Results:EB trauma patients were significantly more likely to experience traumatic brain injury (TBI), intracranial hemorrhage (ICH), or fractures of the head/face compared with MC (54.0% vs. 19.3%, P < 0.0001) and PB (54.0% vs. 33.5%, P = 0.0001) cohorts. EB trauma patients were more likely to lose consciousness than MC or PB trauma patients. Spine fractures (11.0%) were the most prevalent in the EB study population, followed by fractures of the acetabulum (6.0%) and clavicle/scapula/radius (5.0%). EB trauma patients were significantly more likely to be admitted to the intensive care unit (ICU) than MC trauma patients (25.0% vs. 16.0%, P = 0.0256) and were significantly more likely to present as critical trauma activations than PB trauma patients (14.0% vs. 6.0%, P = 0.0106).Conclusions:While the overall injury severity scores are similar to the modes of transport, there are differences in injury severity and patterns. EB accidents have a higher prevalence of TBIs, ICH, and facial fractures. EB trauma patients can result in high utilization of resources because many are critical trauma activations and have a higher rate of ICU stays. These data can help providers better understand injury patterns of EB accidents.

Injuries among children resulting from the use of personal electric mobility devices are common and contribute to a range of adverse societal outcomes, including increased financial strain on healthcare systems, instances of disability, pediatric mortality, and other related consequences. Consequently, investigating the characteristics of pediatric injuries associated with this type of trauma is of paramount importance, as the findings may serve as a foundation for the development and implementation of preventive strategies. Objective. To delineate the structure of pediatric trauma and injury mechanisms associated with personal electric mobility devices across different age groups; to compare injury patterns resulting from electric scooters, hoverboards, and electric bicycles; and to examine the correlation between the use of basic protective equipment and the manifestation of trauma. Materials and Methods. The study utilized forensic medical data, including radiographic and computed tomography (CT) imaging, sourced from inpatient records at the Ivano-Frankivsk Regional Children's Clinical Hospital and forensic medical examinations conducted at the Ivano-Frankivsk Regional Bureau of Forensic Medicine. The sample comprised 50 children aged 4–18 years who sustained injuries while using personal electric mobility devices. Results. Traumatic brain injuries accounted for 72% of cases involving electric scooters, 24% for hoverboards, and 3% for electric bicycles. Neck trauma was observed in 75% of electric scooter cases, 25% for hoverboards, and none for electric bicycles. Upper limb injuries were reported in 45% of electric scooter cases, 48% for hoverboards, and 6% for electric bicycles. Lower limb injuries were documented in 56% of electric scooter cases, 37% for hoverboards, and 5% for electric bicycles. Chest trauma was most prevalent in electric bicycle cases (75%), followed by hoverboards (20%) and electric scooters (5%). The absence of basic protective equipment was found to significantly influence the frequency of injuries among children. Conclusions. The study elucidated the structure of pediatric trauma associated with the use of personal electric mobility devices. The findings demonstrated that the lack of basic protective equipment significantly impacts the incidence of injuries among children across various age groups.

Autonomous robotics emerged as a research and development field nearly forty years ago, but only fifteen years ago, after the DARPA (Defense Advanced Research Projects Agency of the United States Department of Defense) challenge, autonomous mobile systems started to be considered as a solution to the transportation and service problem. This chapter is focused on autonomous (i.e., robotic) vehicles used as human transportation service from two points of view: on one hand, the autonomous vehicle that leads to intelligent transportation systems; on the other hand, autonomous vehicles used for rehabilitation or for enhancing mobility capabilities of their users. Both perspectives of autonomous systems are linked by the use of rapid prototyping techniques, aimed at converting a previously commercial product into a robotic system with a specific transportation usage. This chapter shows, in particular, two cases: two electric commercial vehicles (one golf cart and one car) converted into an autonomous robot for transporting people in cities or for executing specific tasks in sites; and an assistive vehicle (an electric scooter) used by people with reduced mobility. The design of the different components needed to achieve such automation is shown in detail herein.

The growing popularity of electric scooters, hoverboards, and electric bicycles among the pediatric population has led to a significant increase in the number of injuries, requiring detailed study of their formation mechanisms. The study analyzed 84 cases of injury in persons aged 4 to 18 years obtained from medical records and forensic medical examinations, grouped into four age categories. Three main injury mechanisms were established: falling from a moving vehicle, collision with a pedestrian, and collision with an automobile. It has been established that the nature and localization of bodily injuries depend on the type of vehicle, the child's age, speed of movement, and presence of personal protective equipment. For electric scooters, the most typical injuries are traumatic brain injury (72 %), neck injuries (75 %), and lower limb injuries (56 %), with fractures occurring in 66 % of cases. Hoverboards are characterized predominantly by upper limb injuries (48 %) resulting from falls with loss of balance, with significantly lower frequency of traumatic brain injury (26 %). Electric bicycles lead to chest injuries (75 %) due to higher speeds and the possibility of collision with other vehicles. Mathematical and biomechanical models have been developed using classical mechanics equations and the finite element method that allow prediction of the nature of injury depending on fall parameters, the child's anthropometric data, and the vehicle's technical characteristics. The height of the child's center of gravity above the scooter platform ranges from 0.65-0.75 m for children aged 4-6 years to 1.0-1.15 m for adolescents aged 14-18 years. A strong inverse correlation has been established between protective helmet use and the frequency of traumatic brain injury (r=-0.71 for electric scooters, r=-0.58 for hoverboards, p&lt;0.001). The level of protective equipment use among children remains extremely low: only 12 % of children on electric scooters and 35 % on hoverboards used any protective equipment, with less than 5 % using complete protection sets. Age-related features of injury mechanisms were revealed: for children aged 4-6 years, exclusively falls at low speeds are characteristic (100 % of cases), whereas in adolescents aged 14-18 years, the frequency of collisions with automobiles increases (12 %) along with injury severity. A positive correlation between age and injury severity (r=0.68, p&lt;0.01) is explained by higher speeds of movement of older children. Collision with automobiles represents the most severe injury mechanism with the highest lethality, associated mainly with severe traumatic brain injury (42 % of fatal cases), multiple trauma with massive blood loss (31 %), and internal organ damage (27 %). Modeling showed that helmet use reduces peak brain acceleration during impact by 40-60 %, significantly reducing the probability of severe traumatic brain injury, while knee pads and elbow pads reduce fracture risk by 35-45 %. The modeling results can be used in forensic medical practice for establishing injury mechanisms, determining accident circumstances, developing scientifically based preventive measures including age restrictions and mandatory use of protective equipment, and improving expert assessment of injury mechanisms in cases of accidents involving electric personal mobility devices in childhood.

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Shared autonomous vehicles systems (SAVS) are regarded as a promising mode of carsharing service with the potential for realization in the near future. However, the uncertainty in user demand complicates the system optimization decisions for SAVS, potentially interfering with the achievement of desired performance or objectives, and may even render decisions derived from deterministic solutions infeasible. Therefore, considering the uncertainty in demand, this study proposes a two-stage robust optimization approach to jointly optimize the fleet sizing and relocation strategies in a one-way SAVS. We use the budget polyhedral uncertainty set to describe the volatility, uncertainty, and correlation characteristics of user demand, and construct a two-stage robust optimization model to identify a compromise between the level of robustness and the economic viability of the solution. In the first stage, tactical decisions are made to determine autonomous vehicle (AV) fleet sizing and the initial vehicle distribution. In the second stage, operational decisions are made under scenarios of fluctuating user demand to optimize vehicle relocation strategies. To enhance the efficiency of model resolution, the original two-stage robust optimization model is decomposed into separable subproblems, which are transformed using duality theory and linearization. An effective solution is achieved through a precise algorithm utilizing column-and-constraint generation (C&amp;amp;CG). Numerical experiments are conducted on a small-scale network to validate the effectiveness of the model and algorithm. Furthermore, adjustments to the demand fluctuation scenarios are made to assess the impact of uncertain budget levels Γ on the total revenue of SAVS. This research provides AV sharing service operators with an optimal relocation scheduling strategy that balances robustness and economic efficiency.&lt;/div&gt;&lt;/div&gt;

Background In recent years, electric scooters (e-scooter) have emerged as an alternative mode of urban transport due to their availability and effortless use. However, e-scooter-related trauma and injuries, especially to the head, have received wide media coverage and raised public concern about their safety. We aim to determine and compare clinically relevant variables, incidence, and severity between bicycle and e-scooter-related facial fractures and potential protective measures for injury prevention.Material and Methods This retrospective study comprised all patients admitted to a tertiary trauma center with bicycle or e-scooter-related facial fractures between January 2019 and October 2020. Patient- and injury-related variables, including demographics, injury mechanisms, helmet use, influence of alcohol, types of facial injuries, types of other injuries, given treatment, and hospital stay, were collected, analysed, and compared between bicycle and e-scooter injuries.Results Altogether 169 patients with facial fractures, 124 bicycle-related injuries (73.4%) and 45 e-scooter-related injuries (26.6%) were included. Alcohol involvement was significantly higher in e-scooter patients (88.9%) than in bicycle patients (31.5%) (p<0.001). Driving under the influence of alcohol was associated with driving without a helmet in both groups (p<0.001). In multivariate analyses, e-scooter accidents were 18 times more likely to occur under the influence of alcohol (OR 17.85, p<0.001) and were more likely to involve collision with a stationary object (OR 3.81, p=0.028). E-scooter patients were significantly younger (OR 0.95, p<0.001) and had significantly more cranial fractures (OR 10.15, p=0.014) than bicycle patients.Conclusions Compared with patients in bicycle accidents, facial fracture patients injured in e-scooter accidents are younger, are more likely under the influence of alcohol, and sustain more severe craniofacial skeleton fractures. Our results for both groups of patients advocate stricter adherence to helmet and road safety legislation as well as public education for injury prevention. Key words:Electric scooter, bicycle, facial injuries, helmet.

BACKGROUND AND OBJECTIVES:The rapid rise of electric and mechanical bikes and scooters has transformed urban transportation, but their neurosurgical consequences remain underexplored. This study aimed to evaluate micromobility-related injuries over time, examining mechanisms of injury, patient risk factors, injury patterns, and associated clinical outcomes at a Level-1 trauma center over a 5-year period.METHODS:We performed a retrospective review of patients who sustained micromobility-related injuries and presented to the Bellevue Hospital Center between 2018 and 2023. The cohort included riders of electric or mechanical bikes and scooters, as well as pedestrians struck by these devices. Key clinical variables and outcomes were compared across device types, both before and after propensity score matching. Unlike national database studies, this hospital-based analysis provides detailed clinical and neurosurgical outcome data.RESULTS:A total of 914 patients presented with micromobility-related injuries, accounting for 6.9% of all trauma admissions. Annual case volume and electric device involvement increased over time. The most common mechanism was collision with a motor vehicle (49.9%). Most patients (68.7%) required admission; 30.2% required intensive care. The median length of hospital stay was 3 days [IQR 1-5]. Half underwent a surgical intervention or procedure, and the overall mortality was 1.2%. Helmet use was low (31.7%). Pedestrians experienced the most severe outcomes, particularly when struck by electric devices. Injuries clustered during evening hours, suggesting modifiable environmental and behavioral risk factors.CONCLUSION:Micromobility-related trauma imposes a substantial neurosurgical burden, with frequent traumatic brain injury, intensive care unit utilization, and operative intervention. Unlike previous database studies, this hospital-based analysis provides detailed neurosurgical outcome data and identifies prevention targets—including helmet use, intoxication, and urban infrastructure—to reduce morbidity and resource utilization.

When society introduces and accepts new transportation modes, it is important to map risks and benefits. To compare electric scooter (e-scooter) and bicycle injuries. This cohort study is based on prospectively collected data on Norwegian patients who sustained e-scooter or bicycle injuries and presented to an emergency department affiliated with Oslo University Hospital between January 1, 2019, and March 31, 2020. e-Scooter and bicycle injuries were evaluated for associations with sex, age, time of injury, helmet use, intoxication, body region, and injury severity. Descriptive statistics are presented as mean (SD) or number with percentage, with significance set at P < .05 (2-tailed). During the study period, 3191 patients were included (850 e-scooter riders, 2341 bicyclists) with 3839 injuries recorded (997 e-scooter, 2842 bicycle). The mean (SD) age of those injured was 34 (17) years, 2026 riders (63.5%) were male, 1474 (46.2%) were helmeted at the time of injury, and 516 (16.2%) were intoxicated by alcohol or other drugs. The annual incidence of injuries was 120 per 100 000 inhabitants for e-scooters and 340 per 100 000 inhabitants for bicycles. Men were overrepresented in both groups (529 e-scooter riders [62.2%] and 1497 bicyclists [63.9%]). e-Scooter riders were younger than bicyclists (mean [SD] age, 31 [12] vs 35 [18] years). Most injured e-scooter riders were aged 20 to 40 years, whereas injured bicyclists had a broader age distribution. e-Scooter injuries commonly occurred on weekends (378 [46.6%]) and during evening (230 [32.3%]) or nighttime (242 [34.1%]) hours. Most bicycle injuries occurred during weekdays (1586 [69.7%]) and daytime (1762 [61.3%]). e-Scooter riders were more often intoxicated (336 [39.5%] vs 180 [7.7%]) and had a lower rate of helmet use (18 [2.1%] vs 1456 [62.2%]). During nighttime, 230 injured e-scooter riders (91.3%) and 86 bicyclists (69.4%) were intoxicated. e-Scooter riders had more head and neck (317 [31.7%] vs 636 [22.4%]) and lower-limb (285 [28.6%] vs 632 [22.2%]) injuries and fewer upper-limb (341 [34.2%] vs 1276 [44.9%]), thoracic (41 [4.1%] vs 195 [6.9%]), and abdominal, pelvic, and lumbar (13 [1.3%] vs 103 [3.6%]) injuries. In this cohort study, e-scooter riders were younger than bicyclists, did not use helmets, were more often intoxicated, and were more often injured during nighttime. The rate of intoxication among e-scooter riders injured at night was high. Preventive measures, including awareness campaigns, regulating e-scooter availability, improving infrastructure, and implementing stricter helmet and alcohol policies, may prove effective for reducing injuries.

The emergence of shared stand-up electric scooters has led to an increase in their usage and, subsequently, an increase in the incidence of maxillofacial trauma. This study aimed to investigate the trauma pattern associated with the use of stand-up electric scooters compared with that related to the use of bicycles, which was a popular mode of personal mobility before the emergence of stand-up electric scooters. This study investigated the medical records of patients who visited Wonju Christian Hospital for maxillofacial trauma due to the use of stand-up electric scooter and bicycles between November 1, 2017 and October 31, 2022. Maxillofacial trauma was analyzed based on medical records, including those in the evaluation results of teeth, maxillofacial bones, and soft tissues. Crown fractures and tooth avulsions were observed more frequently with the use of stand-up electric scooters than with the use of bicycles. In contrast, crown-root fractures, tooth subluxation, and extrusive luxation were more commonly observed in bicycle riders. Additionally, the proportion of root fractures was similar between the two groups. However, no vertical root fractures were observed in patients who rode bicycles. The maxillofacial bone fracture rates between the two groups were similar, although the fracture patterns were different. The number of patients using stand-up electric scooters is increasing, and they are likely to have a worse prognosis compared with those using existing personal mobility devices.

In this study, a two-stage methodology based on the energy savings gained by optimal network reconductoring was developed for the sizing and allocation of electric vehicle (EV) charging load at the residential locations in urban distribution systems. During the first stage, the Flower Pollination Algorithm (FPA) was applied to minimize the annual energy losses of the radial distribution system through optimum network reconductoring. A multi-objective function was formulated to minimize investment, peak loss, and annual energy loss costs at different load factors. The results obtained with the flower pollination algorithm were compared with the particle swarm optimization algorithm. In the second stage, a simple heuristic procedure was developed for the sizing and allocation of EV charging load at every node of the distribution system utilizing part of the annual energy savings obtained by optimal network reconductoring. The number of electric cars, electric bikes, and electric scooters that can be charged at every node was computed while maintaining the voltage and branch current constraints. The simulation results were demonstrated on 123 bus and 51 bus radial distribution networks to validate the effectiveness of the proposed methodology.

Electric Powered Two Wheeler (EPTW) market has exploded the past decade. The EPTW is mainly an electric bike today but new electric scooters are entering the market as well and will add to the EPTW product mix. China is the dominant user and producer of electric bikes, with 150 million in use, and powerful electric scooters are beginning to appear there. Europe is the second biggest user of electric bikes with over a million sold each year. North American market is tiny in comparison but there is evidence that is to change. The breaking news is that nations in other continents are beginning to explore and are starting to put EBs and ESs into the market. This bodes well that the EPTW will have a respected place in the mix of local transportation that will be affordable and sustainable. EPTW sales will eventually exceed four wheel vehicle sales worldwide. That will be a permanent shift. New versions of the EPTW, like the General Motors/Segway Electric-Network Vehicle, will appear that will enlarge product choice and increase usage. Annual EPTW sales are projected to be 130 million by 2025 and 800 million by 2100. Four Wheel EVs annual sales are projected to grow to 2.5 million by 2025 and 100 million by 2100.

Micro-mobility ride-sharing is an emerging technology that provides access to the transit system with minimum environmental impacts. Significant research is required to ensure that micro-mobility ride-sharing provides a better fulfilment of user needs. In this study, we propose a novel business model for the micro-mobility ride-sharing system where light vehicles such as electric scooters and electric bikes are crowdsourced. This new model consists of three entities, the suppliers, the customers, and a management party, which is responsible for receiving, renting, booking, and demand matching with offered resources. The proposed model has the potential to allow the suppliers to define the location of their private e-scooter/e-bike and the period of time they are available for rent, match it with a particular demand, and then offer suppliers the opportunity to get their e-scooters/e-bikes rented and returned at the end of the renting period to the same (nearby) location. The management party will need to match the e-scooter/e-bike to a series of renting demands with the last demand as a destination very close to the initial location of the e-scooter/e-bike at the start of the renting period. One potential advantage of the proposed model is that it shifts the charging and maintenance efforts to a crowd of suppliers.

A high performance bidirectional DC/AC converter is required for low emission and high efficiency propulsion systems, such as electric vehicles (EVs) and hybrid electric vehicles (HEVs). In order to increase the reliability of the drive system, this paper presents the design, analysis, and implementation of a cost effective sensorless control scheme for the extensively used brushless DC motors and alternators. Taking into consideration cost and ease of implementation, the commutation signals are obtained without the motor neutral voltage, multistage analog filters, A/D converters, and the complex digital phase shift (delay) circuit which are indispensable in the conventional sensorless control algorithms. In the proposed approach, instead of detecting the zero crossing point of the non-excited phase back EMF to the neutral voltage, the commutation signals are extracted directly from the specific average line to line voltages with simple RC circuits and comparators. As a result, the proposed control algorithm can be easily implemented with the low cost CPLDs or 8-bit micro-controllers. Because of the inherent low cost property, the proposed control algorithm is particularly suitable for cost sensitive products such as electric bikes, electric scooters, hybrid electric scooters, home appliances, automotive components, etc. Theoretical analysis and experimental results show that the proposed control algorithm exhibits satisfactory performance over a wide operation range in both motor and alternator operations.