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• E-scooters represent a new transport modality that is smaller in size than an average motorcycle, and one with riding patterns that can be unpredictable. • All shared e-scooter companies offer solely narrow-base e-scooters that force riders to use the foot behind foot riding position. • Foot behind foot riding position is associated with higher rates of orthopaedic fractures. Background: The popularity of electric scooters (e-scooters) and the shared e-scooter services as new means of transportation worldwide led to high numbers of injuries requiring emergency department (ED) visits. Private and rental e-scooters have different sizes and capabilities, offering several possible riding positions. While the rising use of e-scooters and their associated injuries have been reported, little is known about the influence of riding position on injury characteristics. The purpose of this study was to characterize e-scooter riding positions and their related injuries. Methods: E-scooters related ED admissions were retrospectively collected between June 2020 and October 2020, in a level-I trauma center. Demographics, ED presentation data, injury information, e-scooter design, and clinical course were collected and compared according to e-scooter riding position ("foot-behind-foot" vs "side-by-side"). Results: During the study period, 158 patients were admitted to the ED with e-scooter related injuries. The majority of riders used the foot-behind-foot position (n = 112, 71.3 %) compared to the side-by-side position (n = 45, 28.7 %). Orthopedic fractures were the most common injuries (n = 78, 49.7 %). "Foot-behind-foot" group had a significantly higher fracture rate compared to "side-by-side" group (54.4 % vs 37.8 % within group, respectively; p = 0.03). Conclusions: Riding position is associated with different injury types, with orthopedic fracture rates significantly higher in the more commonly used "foot-behind-foot" riding position. Practical Application: These study findings suggest that the common e-scooter narrow-based design is considerably more dangerous, calling for further research to introduce safer e-scooter designs and update recommendations for safer riding positions. [ABSTRACT FROM AUTHOR]

This study aims to analyze the variables that affect the accidents experienced by e-scooter users and to estimate the probability of an accident during travel with an e-scooter vehicle. The data of e-scooter drivers, offered for use via rental application in 15 different cities of Turkey, were run in this study. The methodology of this study consists of testing the effects of the input parameters with the statistical analysis of the data, estimating the probability of an e-scooter accident with machine learning, and calculating the optimum values of the input parameters to minimize e-scooter accidents. By running SVM, RF, AB, kNN, and NN algorithms, four statuses (completed, injured, material damage, and nonapplicable) likely to be encountered by shared e-scooter drivers during the journey are estimated in this study. The F1 score values of the SVM, RF, kNN, AB, and NN algorithms were calculated as 0.821, 0.907, 0.839, 0.928, and 0.821, respectively. The AB algorithm showed the best performance with high accuracy. In addition, the highest consistency ratio in the ML algorithms belongs to the AB algorithm, which has a mean value of 0.930 and a standard deviation value of 0.178. As a result, the rental experience, distance, driving time, and driving speed for a female driver were calculated as 100, 10.44 km, 48.33 min, and 13.38 km/h, respectively, so that shared e-scooter drivers can complete their journey without any problems. The optimum values of the independent variables of the rental experience, distance, driving time, and driving speed for male drivers were computed as 120, 11.49 km, 52.20 min, and 17.28 km/h, respectively. Finally, this study generally provides a guide to authorized institutions so that customers who use shared and rentable micro-mobility e-scooter vehicles do not have problems during the travel process. [ABSTRACT FROM AUTHOR]

We report shared e-scooter ridership and rider perceptions on Virginia Tech's Blacksburg campus before and after introduction of mandatory e-scooter parking corrals in January 2022. The analysis relies on a panel of 131 e-scooter riders surveyed in Fall 2021 and Spring 2022. Although parking corrals were perceived favorably prior to implementation, perceptions became more negative afterwards. Respondents said corrals were not located where needed, difficult to find, fully occupied, and took too much extra time to use. After parking corrals were introduced, ridership declined 72% overall and also fell for all socio-economic subgroups. The heaviest user groups, like undergraduate males, were most likely to quit. The first study identifying desired and actual egress times for e-scooters, we found that roughly two-thirds of riders desired egress times under 2 min and one quarter under 1 min. Prior to the introduction of parking corrals, 82% of riders reported actual egress times under 2 min, and 43% under 1 min. Those who kept riding after the introduction of e-scooter corrals reported longer actual egress times and a stronger stated desire for egress times under 2 min. Communities should be careful when imposing e-scooter parking restrictions to ensure that e-scooter egress time is sufficiently low—ideally within an easy 2 min walk of popular origins and destinations. [ABSTRACT FROM AUTHOR]

Today, the classic means of transport of the environmental alliance (public transport PT, biking, walking) in inner cities are also supplemented by sharing offers such as bike sharing or electrically operated e-car and e-scooter sharing. On the one hand, e-scooters and other micro-mobility services should be considered as part of the public transport system: They cover mobility needs on the first and last mile between home/destination and public transport stop just like bicycle or walking. Keywords: Mobility behavior; mode choice; transport; Covid-19 pandemic; E-Scooter; micromobility EN Mobility behavior mode choice transport Covid-19 pandemic E-Scooter micromobility 369 399 31 11/23/22 20221201 NES 221201 1 Policy background Mobility and transport shape our normal everyday lives, whether it's the daily commute to work, shopping, leisure and holiday trip. Individual transport users continued to use cars or bicycles to a larger extent, while public transport users and multimodal users avoided or changed their routines more often due to infection risks in trains, in public transport or on flights (DLR, 2020b). Due to the currently reported generally low willingness and small numbers of users, the e-scooters do not seem to substitute public transport journeys, but rather to be used as a possibility for travelling to and from the public transport stop or for spontaneous leisure trips. [Extracted from the article]

Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across domestic and urban environments. The driving behavior of e-scooter users significantly differs from other vehicles on the road, and their interactions with pedestrians are also increasing. To ensure pedestrian safety and develop an efficient traffic monitoring system, a reliable object detection system for e-scooters is required. However, existing object detectors based on IoU loss functions suffer various drawbacks when dealing with densely packed objects or inaccurate predictions. To address this problem, a new loss function, balanced-IoU (BIoU), is proposed in this article. This loss function considers the parameterized distance between the centers and the minimum and maximum edges of the bounding boxes to address the localization problem. With the help of synthetic data, a simulation experiment was carried out to analyze the bounding box regression of various losses. Extensive experiments have been carried out on a two-stage object detector, MASK_RCNN, and single-stage object detectors such as YOLOv5n6, YOLOv5x on Microsoft Common Objects in Context, SKU110k, and our custom e-scooter dataset. The proposed loss function demonstrated an increment of 3.70% at A P S on the COCO dataset, 6.20% at AP55 on SKU110k, and 9.03% at AP80 of the custom e-scooter dataset. [ABSTRACT FROM AUTHOR]

This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm's MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient. [ABSTRACT FROM AUTHOR]

Resumen: Introducción: El uso de patinetes eléctricos se ha popularizado como medio de transporte en los últimos años, tanto por su versatilidad como por la practicidad en el uso de estos. Sin embargo, no dejan de ser una fuente de accidentes, muchas veces graves, generando nuevos problemas de salud pública y aumentando los costes de esta. Objetivo: Caracterizar la epidemiología de las lesiones producidas por accidentes en patinete eléctrico y estudiar el impacto económico indirecto en una mutua laboral. Materiales y métodos: Estudio descriptivo, donde se revisan retrospectivamente los pacientes atendidos en urgencias, del Hospital de Asepeyo en Sant Cugat, que han sufrido accidentes en patinete eléctrico en el periodo comprendido entre enero de 2018 y diciembre de 2020. Resultados: Se incluyeron 167 pacientes. El 55% (92) de los pacientes fueron hombres y el 45% (75) mujeres, con una edad media de 37,4 años. Se diagnosticaron 117 fracturas en el 63% (105) de los pacientes. Otras lesiones frecuentes documentadas fueron: 36 policontusiones, 16 TCE y 4 heridas que requirieron sutura. Las lesiones ligamentosas más comúnmente observadas fueron: 4 casos de rotura aguda de LCA, 3 casos de luxación acromioclavicular y 2 casos de rotura del LCC del pulgar. Un total de 105 pacientes requirieron ingreso hospitalario, 3 de ellos en UCI. El 51% (85) de los pacientes requirieron tratamiento quirúrgico. Durante ese periodo, el coste generado como consecuencia de las bajas laborales ascendió a 1.062.444 €. Conclusiones: El patinete eléctrico está comportando un incremento significativo de accidentes de tráfico en población adulta joven. Las lesiones que provocan pueden compararse con las vistas en accidentes de alta energía, generando un alto impacto socioeconómico debido a la naturaleza de las lesiones y sus potenciales complicaciones. Como mutua laboral, el impacto económico generado por las incapacidades laborales ha sido significantemente notable, ascendiendo a más de 1.000.000 € en el periodo revisado. Abstract: Introduction: During the last years, the use of e-scooter has become more and more popular due to the versatility as the practicality in intra-urban mobility. However, e-scooter has turned into a spring of accidents, lot of them, severe, causing new public health problems and significantly increasing the direct and indirect healthcare costs. Objective: To characterize the epidemiology of the injuries caused by e-scooter related accidents and to study their indirect economic impact in work-related accidents hospital. Materials and methods: Retrospective and descriptive study in which has been checked all the medical information about the patients attended in the ER (emergency room) of the Hospital Asepeyo Sant Cugat, who suffered e-scooter accident from January 2018 to December 2020. Results: 167 patients were included in the study. 55% (92) were male and 45% (75) were female. The average of age was 37.4 years-old. There were quantified 117 different fractures in 105 patients (63%). Other injuries documented were: 36 patients with multiple contusions, 16 head traumas and 4 wounds which required suture. The most frequent ligamentous injuries were 4 ACL ruptures, 3 ACJ dislocations and 2 patients with rupture of the UCL of the thumb. 105 patients required hospital admission, 3 of them in ICU. The 51% of the cases required surgical treatment of their injuries. In that period, as a consequence of the work inabilities generated by the total of the patients included in the study, the added cost for the hospital was around 1,062,444€. Conclusions: The popularization of e-scooters between the employed population has caused a high increased number of accidents in this range of age. Most of the injuries observed may be compared with those seen in high-energy traumas. These cause an important socio-economic impact due to the nature of the injuries and the potential complications that could appear. The work inabilities observed as a consequence of e-scooter related injuries in the employed population have caused a substantial economic impact, shooting up to more than 1,000,000€ the indirect cost of our hospital during the reviewed period.

• In the period from 2019 to 2023, 9 e-scooter related fatalities were recorded. • Fatal accidents most often occurred during warm months, the victim was usually man. • Driving on the road and colliding with vehicle were the most common circumstances. • The injuries were usually located in the head area. • The most common cause of death was craniocerebral injuries. With the introduction of mobile applications that allow short-term rentals, electric scooters (e-scooters) are gaining popularity as a means of micromobility in urban areas. The aim of the study was to assess the circumstances and causes of death in traffic accidents involving electric scooters in Poland. The inclusion criteria for the study were met by 9 cases (7 M,2F; mean age: 40.3 years). Accidents usually occur during working days during the warm months, especially during the morning traffic rush hour. Usually, these accidents involved another vehicle (4/9 cases). In addition, the victim was most often the driver (8/9 cases) and rarely uses a helmet (1/9 cases). In only two cases did the test reveal the presence of alcohol in the blood at the time of the accident. In studied cases, head injuries occurred in every case, and injuries to the limbs and chest occurred in more than half of the cases. Within the head, in addition to minor injuries like bruises and epidermal abrasions, skull fractures, and intracranial bleedings predominated. Similar minor injuries were also observed in the extremities, with significant fractures observed only in the lower extremities. Among chest injuries, lung contusions predominated. The most common cause of death was craniocerebral injury (6/9 cases), but there were two deaths each from chest injuries and polytrauma. To increase the safety of e-scooter users, it is recommended that measures be taken to educate users about the potential risks of using the vehicle and that measures be taken to increase the use of helmets, e.g. through legislative action. Further studies involving larger study groups are needed to assess the correlation between potential risk factors and the fatal outcome of the accident. [ABSTRACT FROM AUTHOR]

• Different safety perceptions according to road users are investigated. • The e-scooter riders' safety perspective in overtaking situations is considered. • Genuine safety perception by replaying driving maneuvers is analyzed. • A Unity-based sequential simulation is employed for an immersive driving experience. The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers' viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation. [ABSTRACT FROM AUTHOR]

This study aims to explore the spatial and temporal dynamics of e-scooter usage and its association with the built and social environmental attributes in Minneapolis, Minnesota. Using a 4-month long trip data from 2019, this street-segment-level (N=13,367) study deployed negative binomial regression to build trip generation and destination models. The extent of the impact of built and social environment-related attributes varies at different temporal dimensions of e-scooting. Regarding spatial pattern, university areas and areas close to downtown generated substantially higher trips than other Minneapolis areas. In terms of temporal pattern, trips are more diffused at night and morning, and more concentrated in midday and evening. The high percentage of residential, commercial, and institutional land uses, a higher land use mix, high-valued parcels, and more food-related Points of Interest are strongly associated with e-scooter usage. E-scooter is heavily used in areas with more bike-dependent commuter, less car-dependent commuter, and young-aged people. Further, the presence of a sidewalk, bike-share station, bus stop, and transit shelter on a street segment positively affect scooter usage. These findings will assist e-scooter operators and city planners to formulate policies. Further, by strengthening the literature, this study can serve as a foundation for cities new in e-scooter mobility.

Shared e-scooters have the potential to increase access, complement transit, and replace automobiles, all while reducing emissions and congestion. However, there are concerns worldwide over the mode's safety issues and risks. In this paper, we explore both the motivations and barriers to using e-scooters. Data are collected from a stated preference survey, using a sample consisting of mostly university staff and students in Singapore. Three logit models with varying specifications of e-scooters' speed and lane use and one's prior experience of conflict with a personal mobility device (PMD) are estimated. Overall, the three models have a very comparable fit (adjusted R2 of about 0.55) and consistent results. The results indicate preferences for e-scooters if they are faster and off the sidewalk. However, a bad or unsafe experience with a PMD would negatively affect use to a greater degree, although it varies across individuals. Our study suggests diverting scooters off the sidewalk and increasing the speed may not always be effective in encouraging behavioral shifts toward this alternative mode. Other solutions such as improving the services and enhancing traffic safety should be explored and considered instead. [ABSTRACT FROM AUTHOR]

Background and purpose: The incidence of electric scooter (e-scooter) injuries has increased drastically in numerous countries after widespread availability of shared e-scooters. The economic impact on society from a broader perspective has not been studied. We aimed to estimate the incidence of e-scooter injuries, describe the injury patterns, and estimate the costs of e-scooter injuries. Patients and methods: We performed a retrospective cohort study including all e-scooter-related injuries presented in the three adult emergency departments in Helsinki in 2021. We collected the patient data from the university hospital information system. Injury severity was evaluated based on the Abbreviated Injury Score. The cost of the hospital treatment was analyzed based on our hospital district's service price listing. In addition, we recorded the total amount of sick leave days and estimated their economic impact. Results: In total, 446 e-scooter injuries were identified and taken into the analysis (434 affecting riders and 12 non-riders). The median age of the patients was 26 (IQR 22–33), and 59% were male. 257 (58%) of the of the injuries were minor, whereas 155 (35%) were moderate, 30 (7%) serious, 3 (0.7%) severe, and one (0.2%) critical. Furthermore, 220 (49%) of the patients sustained head injuries. A major spike in accident incidence was seen during the weekend (Friday to Sunday) nights, accompanied by a proportional increase in patients with alcohol intoxication. Including both the costs of the hospital care and absence from work, the approximated total cost of e-scooter injuries was 1.7 million euros, with a median cost of a single accident being 1148 euros (IQR 399–4263 €). Interpretation: Considerable number of the injuries are moderate, severe, or worse. Comprehensive preventive measures must be conducted to decrease the incidence of e-scooter injuries. The use of helmets should be strongly encouraged to prevent severe head injuries. The nighttime bans during weekends and speed limits on e-scooters appear to be justifiable. [ABSTRACT FROM AUTHOR]

The growing popularity of traveling with electric scooters in recent years is, on the one hand, a great opportunity to shift from the current forms of mobility to a new service among the public, but it is also a huge challenge for urban centers and operators. One of the main challenges related to electric scooters is the growing demand for high energy consumption, which is currently a big issue due to the growing interest in electromobility used in mobility as a service (MaaS) systems. However, in order to properly estimate the level of energy consumption of scooters, there is a real need for reliable scientific information on the actual electricity consumption of electric scooters used in shared mobility systems. An extensive literature analysis indicates the lack of research conducted in this area. Noting this research gap, studies were conducted to determine the factors influencing the energy consumption of an electric scooter. The study attempts to determine these values on the basis of the proposed research experiment. The scientific experiment was based on a three-factor plan; an experimental plan. The author conducted the research by driving and measuring electric scooters in order to compare the actual consumption with the data provided by vehicle manufacturers. The result of the research was to determine the influence of factors that influence the significant energy consumption of electric scooters from shared mobility systems. Interestingly, the results generated show that the electricity requirements for six scooters corresponded to one electric car. This proposal raises the question of whether scooters are the best form of electric mobility for the environment. In addition, the work also includes guidelines for scooter users and recommendations for shared mobility operators on the energy consumption of electric scooters. [ABSTRACT FROM AUTHOR]

Electric scooters (e-scooters) are an increasingly popular form of transportation, but their use has also resulted in increased incidence of traumatic brain injury (TBI). Previous reports have predominantly described mild TBI with limited attention to other injury patterns. Our objective was to evaluate the impact of e-scooter use on rates of severe TBI. We performed a multicenter retrospective case review of patients who presented with severe TBI (Glasgow Coma Scale score 3–8) related to e-scooter use and undertook a systematic literature review to identify other reports of severe TBI related to e-scooter use. Of the 19 patients (mean age, 38 ± 16 years; 73.7% male) included in the case series, 13 (68.4%) experienced a fall and 6 (31.6%) were involved in a collision. Various cerebral injury patterns, associated craniofacial fractures, and cervical spine injuries were also seen. Twelve patients (63.2%) underwent intracranial pressure monitor placement and 6 (31.6%) underwent a decompressive hemicraniectomy. Most patients (n = 12; 63.2%) were discharged to acute rehabilitation, with a median modified Rankin Scale score of 2 at 4.9 ± 7.7 months follow-up (52.6% had a good outcome of modified Rankin Scale score ≤2), but 4 patients died of primary injuries. The systematic review identified 18 studies with 77,069 patients between 2019 and 2021, with 37 patients who required intensive care and 6 patients who had neurosurgical intervention. Severe TBI after e-scooter use is associated with high morbidity and is likely underdiagnosed in the literature. Awareness and public policies may be helpful to reduce the impact of injury. [Display omitted] [ABSTRACT FROM AUTHOR]

Introduction: E-scooters recently gained mass expansion, leading to increased use-related injuries, most commonly head trauma, facial, and extremity fractures, while abdominal trauma with kidney involvement is less frequent. Here we present two cases of e-scooter-related high-grade blunt kidney trauma. Case reports: The first case was a 24-year-old male presenting with right abdominal pain after e-scooter autonomous right fall. Focused assessment with sonography for trauma (FAST) was negative, while abdominal CT showed a 3 cm middle-renal laceration. Six-day CT showed minimal urinary extravasation. Neither anemization nor impaired kidney function was observed; the patient was discharged after 9 days. The second case was a 42-year-old male presenting with right flank pain and ipsilateral chest pain after autonomous right fall. Thoracic X-ray revealed multiple rib fractures, while abdominal echography showed a non-homogeneous right kidney with a 1.5 cm perirenal fluid layer. Abdominal CT revealed 2.5 × 4 × 3.5 cm full-thickness middle-upper renal parenchyma laceration and confirmed the perirenal hematoma, while demonstrating two hepatic lesions. A series of CT and ultrasounds confirmed the stability of the aforementioned lesions and reduction of the perirenal hematoma; laboratory findings didn't show anemization nor impaired renal function. The patient was discharged after 10 days. Discussion: Widespread usage of e-scooter is accompanied by an uptick in traumatic events. The chance of renal trauma increases when lateral fall occurs. In our cases patients were hemodynamically stable, the kidney injury severity was high-grade, and non-operative management was effective. Conclusion: E-scooter accidents could lead to high-grade renal injuries, amenable of non-operative management; these events are expected to raise. [ABSTRACT FROM AUTHOR]

• There was no place in the study area where combining an e-scooter and light rail to travel to the downtown university campus was more utilitarian than biking or private car at current travel times and prices. • Black students were 15% less likely than white students to choose e-scooter and light rail instead of car in the stated choice experiment. • Female students were 59% less likely than male students to choose e-scooter and light rail instead of car in the stated choice experiment. • Transit travel time was the strongest direct elasticity to changing the e-scooter and light rail choice probability. • Parking cost was the strongest car mode cross elasticity to changing the e-scooter and light rail choice probability. E-scooters have disrupted and altered the urban mobility landscape. During their introductory period, they have been commonly touted as part of a larger micromobility solution that erases equity barriers and solves the first-mile/last-mile problem. However, few studies in the nascent e-scooter literature have considered these claims. In this study, we used a d-efficient method to design and administer a stated choice experiment (SCE) to students at Portland State University (PSU) (n = 1,968). In the SCE, students were given a three-mode labelled choice set consisting of three travel modes: car, bike, and e-scooter + MAX light rail. (The e-scooter + MAX light rail choice is multimodal, with the e-scooter acting as the first-mile access mode). We generated attitudinal indices using exploratory factor analysis (EFA) in order to control for mode perception. To demonstrate the relationship of travel time and cost in addition to other covariates on mode choice, we estimated a mixed multinomial logit (MMNL) model. We performed elasticity and sensitivity analyses to uncover factors that encouraged first-mile/last-mile e-scooter usage. We then used the model to generate catchment area maps for multimodal trips in the Portland area. Results indicated that e-scooters were lackluster in bringing racial and gender equity in transportation. Additionally, we found that there was no place in Portland where combining an e-scooter and light rail to travel to PSU was most utilitarian compared to bike or private car. This suggests that e-scooters are not perceived as a preferred solution to the first-mile/last-mile problem. Yet, our findings revealed "dials" that can be tweaked through policy measures in order to promote this kind of use, including parking pricing, travel times, fares, and traveler attitudes towards modes. Overall, our analysis of the implementation of e-scooters suggests that their promise is overstated, at least without substantial policy changes to encourage desired use cases. [ABSTRACT FROM AUTHOR]

Electric micromobility represents a sustainable mobility option for specific classes of users and distance thresholds. Had this mobility solution been integrated into a comprehensive mobility framework from the beginning, it would have expanded the coverage and accessibility of urban transit services. Instead, slow and incoherent regulation has established a contrast between enthusiastic users (who consider electric micromobility vehicles "fun" and "easy to use") and recalcitrant public opinion (wherein electric micromobility vehicles are deemed "unsafe" and "dangerous"). Beyond the few attempts made by transport experts to assess the capability of e-scooters to become a sound mobility option (through mobility surveys, pattern analysis, fleet and routing problems), safety and infrastructure design should be developed in a consistent way in order to guarantee a balanced transport setting. With respect to this challenge, a methodology framework is proposed to address the increasing proliferation of micromobility in the context of a coherent transport system. Special attention is devoted to those aspects that have received less attention from the scientific community, namely infrastructure and safe interactions at intersections. The similarities and differences between e-scooters and bikes, chosen in this study as the representative of traditional soft mobility modes, have been taken into consideration. To support the proposed approach, tests investigating e-scooter performance and the perception of both the modes at safety-critical nodes (such as intersections) under different conditions are presented, and the methodology can be applied to a variety of urban scales. The results can be adopted by local authorities, transport companies and e-mobility providers to optimize infrastructure and increase the number and quality of available mobility options. [ABSTRACT FROM AUTHOR]

In this research, the advantages of the e-scooter tool used in the mail or package delivery process were discussed by considering the Turkish Post Office (PTT) data in the districts of Istanbul (Kadıköy, Üsküdar, Kartal, and Maltepe) in Turkey. The optimization Poisson regression model was utilized to deliver the maximum number of packages or mails with minimum cost and the shortest time in terms of energy consumption, cost, and environmental contribution. Statistical and optimization results of dependent and independent variables were calculated using numerical and categorical features of 100 e-scooter drivers. The Poisson regression analysis determined that the e-scooter driver's gender (p|0.05 < 0.199) and age (p|0.05 < 0.679) factors were not effective on the dependent variable. We analysed that the experience in the profession (tenure), the size of the area responsible, and environmental factors is effective in the e-scooter distribution activity. The number of packages delivered was 234 in a day, and the delivery cost per package was calculated as 0.51 TL (Turkish Lira) for the optimum values of the dependent variables. The findings show that the choice of e-scooter vehicle in the mail or package delivery process is beneficial in terms of time, cost, energy, and environmental contribution in districts with higher population density. As the most important result, the operation of e-scooter vehicles with electrical energy shows that it is environmentally friendly and has no CO2 emission. The fact that the distribution of packages or mail should now turn to micro-mobility is emerging with the advantages of e-scooter vehicles in the mail and package delivery. Finally, this analysis aims to provide a model for integrating e-scooters in package or mail delivery to local authorities, especially in densely populated areas. [ABSTRACT FROM AUTHOR]

Introduction: The use of motorized scooters is gaining popularity in Canada and elsewhere. This study aims to summarize characteristics of injuries related to use of motorized scooters using data from the electronic Canadian Hospitals Injury Reporting and Prevention Program (eCHIRPP) and to analyze trends. The eCHIRPP collects information associated with the injury event and clinical information related to treatment (the injured body part, the nature of the injury, injury intent and treatment received) from 11 pediatric and 9 general hospitals across Canada. Results: A free-text search using keywords identified 523 cases related to motorized scooter injuries between January 2012 and December 2019. Most of the injuries reported were among males (62.7%). Fracture/dislocation was the most frequent injury (36.9%), and 14.3% of all patients were admitted to hospital. Joinpoint regression showed a statistically significant increase in injuries related to motorized scooter use between 2012 and 2017 (annual percent change of 18.4%). Conclusion: Study findings indicate the need for continued preventive efforts and improved educational messages on safe riding and the importance of the use of protective equipment to prevent injuries among riders. [ABSTRACT FROM AUTHOR]

The rapidly growing popularity of electric scooters in recent years has allowed the road user to choose another alternative mode of transportation. On the one hand, it is an ecological means of transportation in the city, allowing you to quickly reach your destination; on the other hand, it is a vehicle that causes risk to road safety. Although this is a fairly new mode of transport, it is already of great concern for road safety authorities. E-scooter accidents are recorded with all road users - pedestrians, bicyclists, motor vehicles, other e-scooter riders, or even alone. In this article, the analysis made according to the accident data of 2019-2020 showed that the highest number of accidents occurred between e-scooters and vehicles. Most e-scooter accidents with motor vehicles occur in the intersection zone or during a vehicle turning manoeuvre to (or from) side streets and exit lanes. A descriptive statistical analysis showed that the proportions of the distribution of road accidents between accident participants changed significantly during the analysis period - the number of road accidents between e-scooters and bicycles increased, while the number of accidents between e-scooters and pedestrians decreased. The road accidents between e-scooters and other vulnerable road users are usually caused by sudden, unexpected manoeuvring of road users. Identification of accident schemes and locations is an additional tool for traffic organisation specialists and road safety professionals to prevent accidents, injuries, and fatalities. [ABSTRACT FROM AUTHOR]

Background: Urban mobility has drastically evolved over the last decade and micromobility rapidly became an expanding segment of contemporary daily transportation routines. E-scooter riders and bicyclists may share similar trauma characteristics, but this has been little explored. The objective was to describe and compare the characteristics of e-scooter and bicycle-related trauma.Methods: We conducted a cross-sectional analysis of data from the Rhône road collision registry (January 1, 2019 to December 31, 2019). We included all e-scooter or bicycle riders injured in traffic collisions during the study period; there were no exclusion criterion.Results: A total of 2,779 patients were included; 825 (29.7%) were e-scooter riders and 1,954 (70.3%) were bicyclists. E-scooter riders were younger (median [IQR]: 24 [20-32] vs 29 [20-45] years, p < 0.001) and less frequently male (64.2% vs 73.4%, p < 0.001). Most e-scooter and bicycle road collisions were consequent to a fall or loss of vehicle control (74.2% vs 67.7%, p < 0.001). E-scooter riders were less frequently wearing a helmet at the time of the road collision (6.1% vs 30.7%, p < 0.001) and had more frequently head (24.2% vs 19.9%, p = 0.01) and face (30.6 vs 20.5%, p < 0.001) injuries compared to bicyclists. The median injury severity score was 2 [1-4] in both groups with no significant difference (p = 0.77).Conclusions: E-scooter and bicycle-related trauma patients were mainly young males with minor injuries and most of them sustained a road collision with no third-party. However, they suffered from different injury patterns; e-scooter riders suffered more frequently face and head injuries than bicycle riders, which may be at least partly the consequence of less frequent helmet use among e-scooter riders compared to bicyclists. Hence the two groups of users should not be considered as a single trauma entity. This issue should be promptly addressed to bring down the incidence of preventable injuries and avoid healthcare costs. [ABSTRACT FROM AUTHOR]

Objective: The recent increased use of electric scooters (E-scooter) in daily life has generated a great deal of discussion involving various areas, such as infrastructure problems, legal issues, and rider safety. This study aimed to manage the analysis of the clinical and demographic characteristics of patients who were admitted to emergency departments due to e-scooter accidents, especially during post-legal regulations in Turkey. Methods: The study is conducted as cross-sectional and descriptive research. The notice, data, and documents of the patients included in the study were scanned retrospectively, after which certain measurements were performed, such as the Injury Severity Score (ISS), Abbreviated Injury Scale (AIS), Trauma and Injury Severity Score (TRISS), and Pediatric Trauma Score (PTS) for children aged 15 years or under. Results: The age of the patients included in this study varies from 5 to 76 years, with a mean age of 27.20±11.90 years. When the injury regions of the patients were examined, the results were revealed as follows: 49.6% (n=58) to the upper extremities, 45.3% (n=53) to the lower extremities, 35.0% (n=41) to the head/face, 8.5% 9% (n=10) to the thorax, 0.9% (n=1) to the cervical vertebra, and 0.9% (n=1) to the thoracic vertebra. Considering the correlation between age, hospital admission time, and accident rates in the accidents with trauma scores, no statistically significant association was found between age and AIS (p>0.05) or ISS (p>0.05), and a statistically significant negative correlation of 45.2% was found between age and TRISS (p=0.001; p<0.01). Conclusion: Although it is revealed that E-scooter accidents lead to minor injuries to patients, the use is outside the target audience, causing injury due to mechanical deficiencies and noncompliance with legal rules. [ABSTRACT FROM AUTHOR]

Purpose: To assess the effects of alcohol on the ability to drive an e-scooter, driving tests reflecting real-life situations accompanied by medical examinations focusing on balance were conducted at different blood alcohol concentrations (BACs). Methods: Fifty-seven subjects who consumed alcohol (28 female, 29 male) and 6 consistently sober subjects (3 female, 3 male) participated in the study. Alcohol was administered on a fixed schedule, and the individual drinking quantity was individually calculated in advance using the Widmark formula. Repeated runs through a fixed course were performed. Following each ride, a blood sample was taken for BAC determination, and medical tests were performed. Results: Even at low BACs (0.21–0.60 g/kg), subjects showed a significant decrease in driving performance, to approximately 60% of the initial level. Differences in driving performance at different BAC ranges were observed for different obstacles, especially for the narrowing track, gate passage, slalom, and driving in circles obstacles. Furthermore, worse Romberg and Unterberger test results were correlated with worse driving performance. It cannot be assumed that learning effects during the study had a relevant effect, as shown in the comparison of the driving performance of the alcohol-consuming group with that of the control group. Sex-specific differences were not found. Discussion: Significant deteriorations in driving performance at BACs below 1.10 g/kg confirmed alcohol-related risk potential when using e-scooters. At this time, these findings may lead to the assumption of "relative driving impairment" in Germany. The Romberg and Unterberger tests could be considered a complementary investigation method for the assessment of e-scooter driving impairment. Conclusion: Even at rather low BACs between 0.21 and 0.40 g/kg, there was a significant deterioration in driving performance under the influence of alcohol compared to sober, which highlights the negative effects of alcohol on e-scooter driving. [ABSTRACT FROM AUTHOR]

Hailed as a 'green' solution for decongesting traffic, electric scooters are invading the footpath in numerous cities in Australia. This article argues that legislative and local administrative action taken regarding the use of e-scooters in a space traditionally occupied by pedestrians and slow mobility devices should better balance the rights and interests at stake. [ABSTRACT FROM AUTHOR]

This study explores e-scooter riders' risky behaviour and motivation based on survey, observation, and interview studies. 192 responses from an online survey showed that e-scooter riders' behaviours are categorised into errors, violations, defensive behaviour, and proactive safety behaviour. Statistical analysis revealed significant differences in riding behaviour depending on riders' characteristics and usage patterns. Results from the observation and survey study showed a high frequency of risky behaviours such as not wearing a helmet, riding on the sidewalk against traffic flow, or riding while listening to music to be more representative. Interviews on motivation explained that those behaviours resulted from the trade-off riders found between risky behaviour and the benefits they gained regarding time or comfort. This study posits that the riders' behaviours are distinguished according to the riders' characteristics and usage patterns, as well as different motivational factors. Thus, various strategies should be applied to improve individuals' safety.

E-scooters have become increasingly popular for short-distance travel in urban areas, but this rise in usage also brings about an increased risk of accidents. Studies have shown that approximately 40% of electric scooter accident victims admitted to hospitals suffer head injuries. Therefore, it is crucial to implement safety measures and improve safety systems and equipment to mitigate these risks. One approach to gaining insights into the injuries users face is through simulations using the multi-body method. This method allows for the reconstruction of accidents by modeling and analyzing the dynamic behavior of interconnected bodies. This study aims to assess the impacts on the user's head and the injuries they may sustain in electric scooter accidents using numerical methods. Initially, a reference scenario was established based on a YouTube video, with the assumption that the user was an average-height man. Simulations were conducted for various percentiles, including both males and females. Different velocities were simulated to determine the threshold velocity at which survival becomes practically impossible. Two scenarios were considered: one where the car braked for 0.333 s and another where the distance between the start the braking task and the collision was kept constant. The location of the first head impact on the vehicle was also examined. Injury assessment was conducted using two criteria: Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC). The study found that smaller individuals are more vulnerable to severe injuries, and higher car velocities correlate with more severe user injuries. Furthermore, the location of the first impact varies between genders, with women more likely to experience impacts in the lower part of the windshield, while men tend to experience impacts in the central zone. This study highlights the importance of considering user characteristics and accident dynamics in assessing injury risks associated with e-scooters., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Since the launch of electric scooter (e-scooter) trials in the UK by the Department for Transport (DfT), there has been an upward trend in injuries involving e-scooters. We report a case of a significant orthopaedic injury in a male patient following a fall from an e-scooter. He sustained a right-sided neck of femur fracture. He was treated successfully with a dynamic hip screw. Hip fracture in the young population is rare and is often associated with higher complication rates. We emphasise the importance of prompt identification and operative management of traumatic hip fractures in young patients and raising safety awareness about the use of e-scooters., Competing Interests: Human subjects: Consent was obtained or waived by all participants in this study. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Chu et al.)

The impact of vibrations on health in occupational environments has been extensively studied. Although the effects of vehicle vibrations on driving comfort has been investigated, the literature on the impact of vehicle vibrations on health is scarce. Accordingly, this study aimed to investigate the influence of e-scooter vibrations on driver health by considering both whole-body vibrations (WBVs) and hand–arm vibrations (HAVs). From the perspective of health, vibration zones were defined based on the UNE-2631 and UNE-5349 standards, as well as the European Vibration Directive. Real measurements obtained from an e-scooter acceleration database were used. The results of the study on WBVs show that, on average, 87.54% and 95.47% of non-desirable vibrations are caused by driving an e-scooter on pavers and asphalt, respectively. This shows that ‘potentially non-healthy’ and ‘non-healthy’ vibrations are 25.69% and 61.85%, respectively, when driving on pavers and 85.52% and 12.96%, respectively, when driving on asphalt. Therefore, the WBV levels reached by driving an e-scooter on any pavement could potentially harm health. However, the influence of HAV on the incidence of Raynaud’s syndrome is low. The study results on WBV suggest that future e-scooter designs must be based on a more damped road–driver interface.

Means of transport should be able to fulfil their main function safely and comfortably for travellers and drivers. The effects of vibrations on ride comfort are in the frequency range of 0.5 to 80 Hz and can be analysed using the UNE-2631 standard. This type of analysis has been conducted for several means of transport (bicycles, motorcycles, cars, trucks, etc.), but the literature on e-scooter comfort is very scarce. Existing research describes methodologies, simulation models, and a few measurements related to e-scooter comfort. This paper presents, for the first time, a comfort analysis using an Arduino-based data acquisition system at a sampling frequency of 200 Hz (higher than that in previous studies). Acceleration and speed measurements were obtained by sensorising an e-scooter with inflated wheels without any additional damping systems, which is one of the commonly used e-scooter types. In this study, the comfort for two different speeds (20 and 28 km/h), two types of pavements (pavers and asphalt), and two drivers with different weights was investigated. The results indicate the lowest comfort values for higher velocities and paver pavement. Furthermore, the comfort values were extremely low for all scenarios. In addition, the results demonstrate the necessity of using a sampling rate of at least 80 Hz for this e-scooter model. [ABSTRACT FROM AUTHOR]

Background and objectives: Electromobility has become increasingly popular. In 2001, Segway personal transporters (Segway) were established for tourists, and e-scooters have been in use since their approval in 2019. The aim of this study was to analyze and compare the types of injuries directly related to the use of Segways and e-scooters, respectively, in a German city and to phrase potential safety advice. Materials and Methods: All patients presenting to our emergency department after Segway incidents were retrospectively analyzed and compared with the prospectively collected cohort of patients following e-scooter incidents. Presented injuries were analyzed by body region and injury severity score (ISS). Epidemiological data were collected. Results: Overall, 171 patients were enrolled. The Segway group included 56 patients (mean age 48 years), and the e-scooter group consisted of 115 patients (mean age 33.9 years). Head injuries (HI) occurred in 34% in the Segway group compared to 52% in the e-scooter group. The ISS was approximately equal for both groups (mean ISS Segway group: 6.9/e-scooter group: 5.6). Conclusions: Since the e-scooter group presented a high number of HI along with a higher likelihood and greater severity of HI, mandatory use of helmets is suggested. [ABSTRACT FROM AUTHOR]

Comfort and safe-mobility are two aspects that should be provided to non-motorized users that are an increasing component of the urban micro-mobility (e.g. bikes and e-scooters). For these road users, road pavement unevenness, cracks, potholes and other surface defects can make riding uncomfortable and potentially hazardous. Traditionally, road managers use standardized surveys and Key Performance Indicators (KPIs) of pavement to make decision for maintenance programs and pavement management of road urban network poses challenges in the survey of pavement surface for International Roughness Index (IRI) and distress assessment both for operational and cost constrains. Moreover, there are theoretically limitations of IRI model for low speed urban roads and low-damped vehicles like bikes and e-scooters. In such framework aims of the paper is to investigate the use of smartphone sensors to collect data for the assessment of pavement conditions and definition of KPIs for bike and e-scooter users’ ride comfort and safety. A controlled experiment was performed with repeated runs of a bicycle and e-scooter equipped with a smartphone and an android application was used to collect acceleration and position data. Detailed pavement conditions have been identified with an advanced survey equipment. After data treatments for removing signal noise, adjustment for speed variability and outlier detection, root mean square (RMS) of vertical acceleration signal and weighted frequency content of the vibrations according to ISO 2631–1, have been confirmed suitable KPIs of pavement conditions and comfort rating that can be collected by bikes and e-scooter. Results confirmed the lack of correlation of vibrations in bikes and e-scooter with analogous parameters collected with car as probe vehicle and with IRI standard values, as well. Instead, pavement monitoring by bikes and e-scooter can provide effective detection of typologies and severities of distresses not detectable by similar approaches in damped vehicles. Good correlations have been identified between RMS and medium severity alligator, longitudinal and transversal cracks. High severity pavement distress and potholes have been identified by outliers in RMS.

In Turkey, the transportation industry's greenhouse gas (GHG) emissions increased by 147.1% between 1990 and 2019. Today, this transportation industry (i.e., freight and passenger) is among the significant contributors to greenhouse gas emissions in Turkey's megacities. Moreover, 65.43% of short-distance trips between home to work and home to school have been made by private automobiles in Istanbul and increasing concerns about environmental pollution have led practitioners to seek practical, robust, and effective solutions to reduce GHG emissions. Shared electric scooters have rapidly become popular for end-users and practitioners in megacities, depending on their valuable advantages. However, the rapid spread of micro-mobility, characterized by e-scooters, has also raised questions about this system's sustainability, suitability, and applicability. Thus, there are some critical and noteworthy gaps in this issue. This study investigates the factors affecting the suitable e-scooter selection for a sustainable urban transport system. Besides, it aims to develop a methodological framework for assessing the available e-scooter alternatives. For this purpose, a novel negotiation approach, a new form of the Delphi technique, was developed with the help of Fermatean fuzzy sets to identify the influential criteria. Also, the current paper presents a consensus-based MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) decision-making model based on Fermatean fuzzy sets to address the appraisal problem concerning e-scooter selection. The current paper indicated that economic measures such as acquisition price and upkeep costs affect the e-scooter selection processes. In addition, an optimization model based on cross-entropy and dispersion measures is utilized to compute criteria weights. It highlighted that the costs of e-scooters are still high, and operators consider these criteria instead of the technical and operational features of the e-scooters. Finally, the validity check executed to test the robustness and trustworthiness of the model affirms the model's firmness and trustworthiness. [ABSTRACT FROM AUTHOR]

Shared micromobility in the U.S. has rebound after the decline caused by the COVID-19 pandemic, with a substantial increase in the adoption of shared e-bikes nationwide. However, research on hybrid e-bike sharing, which combines station-based and dockless systems, is limited. This study addresses this gap by comparing spatial determinants of hybrid e-bike and dockless e-scooter sharing link flows in 32,965 street segments in Portland, Oregon during 2022, using gradient boosting decision tree (GBDT) models. Distance to the city center emerges as the most important determinant for both modes, with closer proximity to the city center associated with higher link flows. Factors such as the presence and types of bike facilities, the availability of streetlights and street trees, and job density also significantly influence e-bike and e-scooter link flows. A notable difference between the two modes is that e-scooter trips are more sensitive to distance to the city center than e-bike trips. Furthermore, bike facilities have a greater impact on e-bike link flows, whereas job density is more influential in determining e-scooter link flows. These findings offer strategies for policymakers and urban planners to promote and manage shared micromobility and optimize the built environment. These strategies include enforcing higher device availability requirements in underprivileged neighborhoods, transitioning e-scooter sharing systems into a hybrid model, expanding the off-street bike trial network and bikeway network, and augmenting the coverage of streetlights and street trees along the bikeway network. • Distance to the city center is the most important determinant for both modes • Bike facilities, streetlights, street trees, and job density are also key factors. • E-scooter trips are more spatially concentrated than e-bike trips. • Bike facilities are more crucial for e-bike than e-scooter link flows. • Job density is more crucial for e-scooter than e-bike link flows. [ABSTRACT FROM AUTHOR]

• E-scooter-related orthopaedic injuries are frequently seen in the District General Hospital setting. • E-scooter orthopaedic injuries constitute a significant burden to hospital resources through additional emergency department attendances, surgical procedures, inpatient admission and prolonged inpatient stays in addition to further subsequent resources utilised in outpatient follow-up. Electric scooters (e-scooters) are an increasingly popular method of transportation worldwide. However, there are concerns regarding their safety, specifically with regards to orthopaedic injuries. We aimed to investigate the overall burden and financial impact on orthopaedic services as a result of e-scooter-related orthopaedic injuries. We retrospectively identified all e-scooter-related injuries requiring orthopaedic admission or surgical intervention in a large District General Hospital in England over a 16-month period between September 2020 and December 2021. Injuries sustained, surgical management, inpatient stay and resources used were calculated. Seventy-nine patients presented with orthopaedic injuries as a result of e-scooter transportation with a mean age of 30.1 years (SD 11.6), of which 62 were males and 17 were females. A total of 86 individual orthopaedic injuries were sustained, with fractures being the most common type of injury. Of these, 23 patients required 28 individual surgical procedures. The combined theatre and recovery time of these procedures was 5500 min, while isolated operating time was 2088 min. The total cost of theatre running time for these patients was estimated at £77,000. A total of 17 patients required hospital admission under Trauma and Orthopaedics, which accounted for total combined stay of 99 days with a mean length of stay of 5.8 days. While there are potential environmental benefits to e-scooters, we demonstrate the risks of injury associated with their use and the associated increased burden to the healthcare system through additional emergency attendances, frequent outpatient clinic appointments, surgical procedures, and hospital inpatient admissions. [ABSTRACT FROM AUTHOR]

• Extended the Technology Acceptance Model for continuance use of shared e-scooters. • Conducted a survey and collected information of 2126 e-scooter users in Chicago, IL. • Employed a PLS-SEM to model the key drivers of e-scooter usage in the future. • Perceived usefulness and perceived reliability are the most critical factors. • Social influence, variety seeking, and perceived enjoyment are significant factors. As e-scooters become more popular, service providers and policymakers are seeking ways to retain the existing customers and encourage them to continue to use e-scooters in the future. In this study, we extend the concepts of the technology acceptance model to identify the factors that affect the intention to continue using e-scooters. We build our findings based on survey data including 2126 shared e-scooter users in Chicago. Using Partial Least Squares Structural Equation Modeling, we analyzed the data and 10 proposed hypotheses. Our empirical results substantiate that the proposed model provides a theoretical framework to understand the continuance intention of shared e-scooter users. According to the findings, the most salient factor determining users' decisions is perceived usefulness, followed by perceived reliability. The significance of reliability necessitates taking measures to guarantee the availability of e-scooters in times and places they are needed, particularly for mandatory trips. Additionally, social influence, perceived ease of use, variety seeking, and perceived enjoyment, are evinced to represent the other critical drivers of using e-scooter in the future, and in order of precedence. The insights from this study can assist shared e-scooter operators, transportation planners, and policymakers in making informed decisions and pave the way for a greater inclination to continue using shared e-scooters and move toward smart cities. [ABSTRACT FROM AUTHOR]

Dockless e-scooter sharing, as a new shared micromobility service, has quickly gained popularity in recent years. In this paper, we present a practical approach to estimating e-scooter flow patterns without knowing the actual routes taken by the e-scooter riders. Our method takes advantage of a huge open dataset that contains the origins and destinations of millions of trips. We show that our models can help cities better support the emerging shared micromobility service. The additional information generated in the modeling process can also be useful for a more refined analysis of e-scooter trips. [ABSTRACT FROM AUTHOR]

Background: Electric scooters (e-scooters) have become popular within a short time. With its growing popularity, the number of admissions to emergency services due to e-scooter injuries has also increased. In this study, we aimed to analyze the clinical and demographic characteristics of e-scooter injuries applied to our emergency department. Methods: In this study, demographic data of e-scooter injuries, injury type and pattern, usage of the helmet and protective clothing, and pathological findings, which were detected as a result of injury, were analyzed retrospectively. Results: We analyzed 70 e-scooter injuries (mean [SD] age 25.82 ± 8.04 years; 52.9% male). The most common reasons for admission to the emergency department were soft tissue trauma with a rate of 45.7% (n = 32) and head trauma with a rate of 40% (n = 28). Based on the performed examinations, orthopedic fractures and sprains were detected in 18.5% (n = 13) of the patients, while maxillofacial fractures were detected in 11.4% (n = 8). Helmet use was detected in 4.3% (n = 3) of e-scooter users, while 2.9% (n = 2) of the patients had a blood alcohol level of > 10 mg/dl. In our patient group, 4.3% (n = 3) were transferred to the ward and 1.4% (n = 1) to the intensive care unit. Conclusions: We consider that serious injuries can be prevented by establishing legal regulations regarding the use of e-scooters and determining the appropriate routes for e-scooters, hence making e-scooters a safer means of transportation. [ABSTRACT FROM AUTHOR]

Shared e-mobility is a category of emerging mobility services that includes electric carsharing, e-bike sharing, and e-scooter sharing. These services are expected to reduce the negative externalities of road transport in cities, which is currently dominated by fossil-fuel-powered private car trips. In order to better inform the development and promotion of these services and indicate directions for further research, we conducted a comprehensive review of existing literature on the three shared e-mobility modes focusing on their usage pattern, demand estimation, and potential impacts. We found that despite the different vehicle capabilities, all three shared e-mobility services are mainly used for short trips, and their current users are mostly male, middle-aged people with relatively high income and education. The demand of all shared e-mobility modes share many common predictors: they appeal to people with similar socio-demographic characteristics and generate higher demand in locations with better transport connectivity and more points of interest. Shared e-mobility services can potentially lead to positive impacts on transportation and the environment, such as reducing car use, car ownership, and greenhouse gas emissions. However, the magnitude of these benefits depends on the specific operational conditions of the services such as the fuel type and lifetime of shared vehicles. The impact of each shared e-mobility mode is also expected to be affected by other coexisting shared e-mobility modes due to both complementarity and competition. Future directions should include studying the competition between and integration of multiple shared e-mobility modes. [ABSTRACT FROM AUTHOR]

Background and Objectives: In 2019, electric scooters (e-scooters) were legalized for use on German roads. Their popularity is attributable to their relatively cheap and easy availability. E-scooters had already been established in other countries for some time. A rising number of crashes involving e-scooters have been reported from different countries, but data about crashes in Germany are still limited. Materials and Methods: Our local accident research unit documented prospectively a statistically randomized sample of e-scooter crashes between July 2019 and December 2020. Collected data include, but is not limited to, injury details as well as technical characteristics and circumstances of the crashes. Results: Twenty-three crashes are included in this study. Two drivers (8.70%) were uninjured and 60.87% (n = 14) suffered only minor injuries (Maximum Abbreviated Injury Scale [MAIS] 1). Four drivers (17.39%) sustained a MAIS 2 and three drivers (13.04%) a MAIS 3. 41 injuries in total were recorded among the 23 patients included in this study. The Injury Severity Score ranged from 0 to 22. In our cohort, the main person responsible for the crash was most often the e-scooter driver him/herself (n = 20, 86.96%). In n = 6 (26.09%), a positive alcohol test was carried out, and one driver (4.35%) was under the influence of cannabis. Conclusions: Crashes involving e-scooters have the potential to cause severe injuries and e-scooters should not be considered harmless gadgets. The prevalence of drug and alcohol use during e-scooter driving and the high percentage of e-scooter drivers being responsible for the crash may indicate potential recklessness on the part of the drivers.

Over the past few years, increased e-scooter ridership has raised concerns about the growing number of injury accidents involving e-scooters. Additionally, given the lack of appropriate e-scooter accident data, the extent to which built environment and socioeconomic factors affect e-scooter safety is unclear. In consideration of these issues, this study was aimed at identifying the factors contributing to the number of e-scooter injury accidents in Austin. We developed zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models on the basis of 2018 dockless e-scooter injury accident data collected from the Patch platform. The results indicated that the ZIP model better fit the accident data. Significant variables included the ratio of 18- to 34-year-old males to their female counterparts, the median annual household income (in thousands), the ratio of public transport users to private transport users, the land use entropy index, the percentage of restaurants, and the percentage of educational centers in the study site. As e-scooter accidents are likely to occur in dense urban settings, a critical initiative is to develop new infrastructure, such as bike lanes, and/or extend sidewalks beyond core urban areas. Another highly recommended measure is to implement a demerit point system for the suspension of riders who engage in unsafe behaviors. Lastly, launching educational campaigns by e-scooter operators and law enforcement agencies will raise riders' awareness about road and personal safety. [ABSTRACT FROM AUTHOR]

BACKGROUND: To determine the frequency, characteristics, and use of resources related to electric scooter (e-scooter) injuries in the emergency department (ED) of a major metropolitan area hospital. METHODS: We performed a retrospective review of all ED presentations related to e-scooter injuries at a level I trauma center between May 2017 and February 2020. We identified ED presentation data, injury-related data, patients' clinical course after evaluation, injury diagnosis, surgical procedures, and ED readmissions. RESULTS: A total of 3,331 patients with e-scooter injuries presented to the ED over a 34-month period. There was a 6-fold increase in e-scooter-related injuries presenting to the ED, from an average of 26.9 injuries per month before the introduction of shared e-scooter services in August 2018 to an average of 152.6 injuries per month after its introduction. The average injury rate during weekdays was 3.27 per day, with the majority of injuries occurring in the afternoon. The most common mechanism of injury was rider fall (79.1%). There were a total of 2,637 orthopedic injuries, of which 599 (22.7%) were fractures. A total of 296 (8.9%) patients were hospitalized following the initial ED admission, and 462 surgeries were performed within 7 days of ED arrival. CONCLUSIONS: The introduction of the shared e-scooter services is associated with a dramatic increase in e-scooter injuries presenting to the ED. E-scooter use carries considerably underestimated injury risks of high-energy trauma and misunderstood mechanisms of injuries. These injuries challenge the healthcare system, with a major impact on both EDs and surgical departments. [ABSTRACT FROM AUTHOR]

(1) Background: Electric micro-mobility vehicles (i.e., e-bikes and e-scooters) represent a fast-growing portion of the circulating fleet, leading to a multiplication of accident cases also attributable to risky behaviours adopted by the riders. Still, data on vehicle performance and rider behaviour are sparse and difficult to interpret (if not unavailable). Information regarding the overall accident dynamics can, however, aid in identifying users' risky riding behaviour that actually led to a harmful event, allowing one to propose efficient strategies and policies to reduce the occurrence of road criticalities. (2) Methods: Speed and acceleration data of six cyclists of traditional and electric bikes were extracted from six closed-circuit experiments and real road tests performed in the city of Florence (Italy) to derive their behavioural patterns in diverse road contexts. (3) Results: The application of analysis of variance and linear regression procedures to such data highlights differences between men and women in terms of performance/behaviour in standing start; additionally, the use of e-bikes favours a higher speed ride in correspondence to roundabouts and roads with/without the right of way. To thoroughly assess the rider's responsibilities in an eventual accident, an ancillary procedure was highlighted to evaluate whether a micro-mobility vehicle complies with the applicable regulations. (4) Conclusion: With these results, the prospective recognition of rider behaviour was facilitated during the investigation process, and the abilities to extract such relevant information from in-depth accident data wereconsequently enhanced. [ABSTRACT FROM AUTHOR]