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Analysis of experimental data on dynamics and behavior of e-scooter riders and applications to the impact of automated driving functions on urban road safety

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... For example, an e-scooter driver monitoring system can be used to detect when e-scooter riders are unstable, unfocused or distracted when riding (Kim, Ryu, Oh, & Kang, 2022). Since limited awareness of rules and low-risk perception may lead to such risky rider behaviour, simple interventions have been proposed, such as pre-ride free training sessions (Brunner, Locken, Denk, Kates, & Huber, 2020;Glenn et al., 2020) and warning messages on smartphone screens before unlocking the app (Smit, Graham, & Erasmus, 2021). Many leading e-scooter companies provide users with information about traffic regulations and safety precautions prior to starting their trips. ...
... This benefit applies both to their initial experiences and as they become more frequent riders over time. Within a short period, complete beginners learned V. Mehranfar and C. Jones Transportation Research Part F: Psychology and Behaviour 107 (2024) 321-382 to master driving manoeuvres and stabilise their e-scooters in a simulating environment, which suggests that mandatory training sessions before the first ride can make a significant contribution to preventing e-scooter injuries (Brunner et al., 2020). A German study also recommends mandatory driving tests to ensure a basic understanding of traffic rules before using e-scooters, as they found that solo riders were the primary cause of accidents (Brauner et al., 2022). ...
... To target private e-scooter riders, these messages could be communicated by providing local rules during the purchase process. It is also suggested that mandatory training sessions be held before the first e-scooter rides (Brunner et al., 2020;Glenn et al., 2020). The requirement for a license or insurance for e-scooter use has also been suggested in prior studies (Bai et al., 2015;Salas-Niño, 2022). ...
Article
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The proliferation of e-scooters in urban spaces has introduced safety concerns despite their potential to reduce traffic congestion and provide an environmentally friendly solution for short-distance trips. This study consolidates existing knowledge on e-scooter safety through a systematic literature review of 168 academic studies and grey literature, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our primary objective is to identify the key e-scooter safety concerns from existing literature, together with the strategies stakeholders use to address these concerns, and highlight areas for further research. . The literature shows that e-scooter riders are commonly injured in single-vehicle incidents, with a clear association between severe injuries and violations of traffic rules such as speeding and alcohol consumption. Frequently recommended safety measures include separating e-scooters from pedestrians, instituting licensing and mandatory training, and enforcing helmet usage and zero alcohol consumption. On top of that, clear legislative definitions for e-scooters ease and improve enforcement, and setting technical requirements for e-scooter design can improve stability, handling performance, and reduce incidents. Understanding the differences between user types and the underlying factors influencing risky behaviour is crucial for developing effective interventions. Users of shared schemes often lack knowledge of rules and have poorer riding skills, possibly due to their less frequent use. Conversely, private e-scooter owners pose enforcement challenges for speeding and prohibited riding, as these scooters lack geofencing and tracking capabilities often found in shared scheme escooters. Helmet non-use, where mandatory, is attributed to a lack of support from riders for increased law enforcement and a low perception of risk rather than a lack of knowledge about the laws. Similarly, illegal sidewalk riding is linked to factors of comfort and convenience rather than infrastructure preference or unawareness of illegality. Proactive measures that are user-based, time-based, and location-based require further investigation. Consistently collecting and analysing data informs region-specific safety decisions and allows policymakers to monitor safety risks over time and assess intervention effectiveness, which are largely absent in current literature.
... This approach is in line with some other studies in the literature [25] that, starting from the analysis of crash reports, attempted to reproduce e-scooter crash patterns through simulations for the aim of safety assessments [26] or to investigate the most common injury mechanisms to propose specific policies (such as mandatory helmets [27]). Clearly, this ...
Article
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The remarkable impact that e-scooters have had on the transportation system drives research on this phenomenon. The widespread use of e-scooters also poses several new safety issues, which should be necessarily studied. The aim of this paper points in this direction, investigating the main contributing factors, causes, and patterns of recorded e-scooter crashes, considering also different crash types and severity, using the City of Bari (Italy) as a case study. The crash dataset based on police reports and referring to the period July 2020–November 2022 (i.e., the first period of e-scooter implementation in the City of Bari) was investigated. Crashes were clustered according to several variables. No fatal crashes occurred, even though crashes mostly resulted in injuries (70%). Considering road type, divided roads were found to be less safe than undivided ones, due to higher mean speeds than on other roads and to a less constrained e-scooter driving behavior. Calm (off-peak) daytime hours seem to lead to more frequent e-scooter crashes with respect to both peak and nighttime hours, even if the latter hours are associated with an increased severity. Once controlled for exposure, season, lighting conditions, and the private/sharing ratio do not seem influential. E-scooters are more prone to be involved in single-vehicle and pedestrian crashes at segments than other vehicles, but they show similar crash trends than other vehicles (i.e., angle crashes) at intersections. As emerged from traffic surveys, not all e-scooter users were found to use cycle paths. Combining this information with crash data, it seems that not using cycle paths is considerably less safe than using them. Besides engineering measures and policies, awareness campaigns should be promoted to elicit safe users’ behavior and to tackle the several violations and misbehaviors emerging from the crash data.
... The comparison of the simulation results between the proposed model and the original model indicated the superior predictive capabilities of the modified SFM, especially for accurately capturing e-scooter interactions with a pedestrian crowd. Except for the model-based e-scooter behavior simulation, Brunner et al. [12] conducted laboratory experiments by attaching the inertial measurement units (IMUs) to the e-scooters and obtained the movement data with implications for the escooter stability feature. The data also captured the kinematic and behavioral characteristics of e-scooter riders. ...
... Boglietti et al. [16] conducted an experimental comparison in terms of ride comfort between an e-scooter and an e-bike driving on homogeneous road surfaces. Brunner et al. [17] analysed the behaviour of the rider performing common manoeuvre in a controlled indoor testing facility. Cafiso et al. [18] focus the attention on ride comfort and safety in relation to the road maintenance. ...
Article
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In recent years, micro-vehicles have been increasingly involved in urban mobility following the actual trend towards light, more affordable, and eco-friendly means of transportation. Among this vehicle category, the electric kick scooters (e-scooters) represent the most popular example driven by app-based sharing mobility services. Despite the positive implications, poor safety requirements and issues of discomfort are also related to this new segment. The recent spread of e-scooters is motivating the scientific community in investigating performance and ride comfort, in the attempt of improving vehicle design and safety regulations. The aim of this study is to evaluate e-scooter vibrations in driving in a realistic environment, constituted by bike path with seven speed bumps. Fourteen healthy young participants (seven males and seven females) are asked to conduct the test at two different constant velocities ( 5 5 km/h and 25 25 km/h). Accelerations are acquired at the main human body segments as well as on the e-scooter. The assessment is based on identifying maxima and root mean squares from signal time histories. A non-parametrical statistical analysis is performed focusing on vibrations transmitted from vehicle to human body, e-scooter velocity, and some rider’s characteristics such as gender, mass, dominant arm, and dominant foot. Root mean squares and tests at low velocity generally underline a larger number of significant differences. Moreover, the parameter which mostly influences the system is the rider’s mass. Overall, the proposed methodology proves to be an efficient tool to investigate the vehicle-rider vibrational influence.
... However, the lack of basic data on electric scooters has caused difficulty in analyzing various aspects of electric-scooter accidents, which have increased rapidly in a short period of time, and consequently, in solving this problem. Thus, several studies have been conducted to analyze basic data on electric scooters [3][4][5][6][7][8][9][10]. ...
Article
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Electric scooters are quickly becoming a popular form of mobility in many cities around the world, which has led to a surge in safety incidents. Moreover, electric scooters are not equipped with safety devices for riders, which can lead to serious accidents. In this study, a footrest, data-collection module, and accelerometer module for electric scooters were developed to prevent various accidents caused by the rapid increase in the use of electric scooters. In the experiment, the boarding data of the electric-scooter riders were collected from the footrest and data-collection module. Moreover, the driving data of the electric scooters for different road types were collected with the accelerometer module. We then trained two artificial intelligence (AI) models based on convolutional neural networks (CNNs) for different types of data. When we considered the learning accuracy and mean square error (MSE), which are performance indicators of the ability of the trained AI models to discriminate data, for each AI model, the learning accuracy converged to 100% and the MSE converged to 0. Further, this study is expected to help reduce the accident rate of electric scooters by resolving the causes of frequent accidents involving electric scooters around the world.
... Garman et al. [19] designed a test course to simulate the urban environment and typical maneuvers, considering riders with different anthropometries and driving experiences. E-scooter riders are studied by Brunner et al. [20] from the perspective of Vulnerable Road Users, with the aim of accident reduction: a controlled campaign is completed to evaluate lateral stability and the driving behavior of beginners. Paudel [21] presented a theoretical analysis of self-stability properties in relation to velocity and acceleration; moreover, the author studied lateral steady-state performances of e-scooters, pursuing a comparison with traditional bikes. ...
Article
Electric(e)-scooters have emerged as a popular, ubiquitous, and first/last-mile micromobility transportation option within and across many cities worldwide. With the increasing situation-awareness and on-board computational capability, such intelligent micromobility has become a critical means of understanding the rider's interactions with other traffic constituents (called Rider-to-X Interactions, RXIs), such as pedestrians, cars, and other micromobility vehicles, as well as road environments, including curbs, road infrastructures, and traffic signs. How to interpret these complex, dynamic, and context-dependent RXIs, particularly for the rider-centric understandings across different data modalities --- such as visual, behavioral, and textual data --- is essential for enabling safer and more comfortable micromobility riding experience and the greater good of urban transportation networks. Under a naturalistic riding setting (i.e., without any unnatural constraint on rider's decision-making and maneuvering), we have designed, implemented, and evaluated a pilot Cross-modality E-scooter Naturalistic Riding Understanding System, namely CENRUS, from a human-centered AI perspective. We have conducted an extensive study with CENRUS in sensing, analyzing, and understanding the behavioral, visual, and textual annotation data of RXIs during naturalistic riding. We have also designed a novel, efficient, and usable disentanglement mechanism to conceptualize and understand the e-scooter naturalistic riding processes, and conducted extensive human-centered AI model studies. We have performed multiple downstream tasks enabled by the core model within CENRUS to derive the human-centered AI understandings and insights of complex RXIs, showcasing such downstream tasks as efficient information retrieval and scene understanding. CENRUS can serve as a foundational system for safe and easy-to-use micromobility rider assistance as well as accountable use of micromobility vehicles.
Article
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In this work, a detailed multibody model of an electric kickscooter is presented. The model includes toroidal wheels as well as rear and front suspensions. The equations of motion are derived and linearized along the steady forward motion of the vehicle. Using an efficient linearization approach, suitable for complex multibody systems with holonomic and nonholonomic constraints, allows for obtaining the reduced linearized equations of motion as a function of the geometric, dynamic, wheels’, and suspensions’ parameters. The proposed electric kickscooter multibody model is validated with the stability results of a previously presented electric kickscooter benchmark. Since the resulting eigenvalues are parameterized regarding the design parameters, a detailed linear stability analysis of the system is performed. In particular, the influence on the stability of the toroidal geometry of the wheels, the elliptic cross-section of the toroidal wheels, the rider model, the steering axis inclination angle, the inertia tensor of the front frame, and the rear and front suspensions is analyzed. The model presented, together with the linearized equations of motion obtained in this work, enables a systematic analysis of the stability of these vehicles, which helps design new electric kickscooters with improved vehicle safety conditions and oriented to a wider range of potential users.
Article
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Introduction E-scooters have made a place for themselves on urban roads as an affordable, easy-to-use and environmentally friendly method of transportation. However, and partly because of their road behaviors and safety outcomes, e-scooter users have started to represent a focus of attention for transport planners and policymakers. Aim The present systematic review aims to target and analyze the existing studies investigating the psychosocial characteristics of e-scooter riders, focusing on their behavioral and risk-related features. Methods For this systematic review, the PRISMA methodology was used, which allows for the selection of suitable papers based on the study topic, in accordance with a set of pre-defined criteria and a search algorithm. A total of 417 indexed articles were filtered, resulting in only 32 eligible original articles directly addressing the issue. WOS, Scopus, NCBI, Google Scholar, and APA databases were used to create and test search techniques. Results At the literature level, most of the existing studies are distributed in a few regions of the globe. At the user’s level, results show how e-scooters are most commonly used by young, highly educated, urban-dwelling males, usually for short trips. In regard to road behavior, individuals with the lowest degrees of risk perception remain more prone to engaging in risky road behaviors likely to increase their crash involvement. This might be worsened by the lack of normative e-scooter regulations (and their enforcement) in many countries, plus the marked absence of road training processes. As common limitations, it can be mentioned that 87.5% of these studies used self-report methods, while 59.4% had local coverage. Conclusions The findings of this systematic review endorse the growing need to develop and enforce traffic laws and training processes for e-scooter users. In addition, road safety education and training programs are highlighted by existing studies as potentially pertinent alternatives to increase risk perception, and reduce risky behaviors, road conflicts and crash likelihood among e-scooter riders.
Conference Paper
Micro-mobility modes such as e scooters are gaining increasing popularity, but e-scooter riders have high crash/injury risks. Virtual assessment of traffic safety measures, such as automated driving functions, requires valid models of all road users, including their interactions and responses to threats. This paper focuses on process models of threat response and crash avoidance of e-scooter riders, a crucial aspect of safety assessment. The response latency and the capability for crash avoidance vary considerably among e-scooter riders. We propose threat response models involving cognitive, dynamic and kinematic subprocesses. These models define key parameters that determine threat response characteristics. To capture the individual variability of these features, we present a study in which 36 subjects performed e-scooter riding tasks designed to reproduce the subprocesses required for crash avoidance. The measurements were supported by a semantic questionnaire. The key measured parameter distributions included response times, longitudinal decelerations, lateral accelerations, attainable curvatures and times required for countersteering. Although subjects were clearly motivated to realize their best performance in the e-scooter riding tasks, the proportions of poorer performances (e.g., high latencies, slow braking, or inadequate dodging maneuvers) and their magnitudes are of crucial importance for simulative assessment of safety impacts of automated driving or other traffic measures. The identified characteristics can be used in traffic simulations to represent e-scooter behavior in general and, in incipient conflict situations, to simulate threat response processes, including the failure of braking or dodging maneuvers, with a high degree of realism.
Technical Report
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In February and March 2019 we invited people, whether they had used an e-scooter or not, to tell us what they thought about e-scooter use in New Zealand. At that point, Lime e-scooters were available in four areas of New Zealand: Auckland, Hutt Valley, Christchurch, and Dunedin. Prior to the arrival of Lime, e-scooters had not been widely available, but around the end of 2018 and the beginning of 2019 Lime e scooters arrived, and e-scooters became prominent in practice and in conversation, were regularly featured in media stories, and became increasingly available for personal purchase. Our first survey aimed to capture early data about the use and perceptions of e-scooters and we intend subsequent surveys to explore how the situation evolves over time. A survey of attitudes to and use of e-scooters in New Zealand cities received 591 useable responses from a convenience sample, distributed through interest groups and social media. - Respondents to the survey were more likely to be young adults, NZ European, to have a high level of education, and a high income; most were from Christchurch. - Although not representative of the population, the survey provides some insights into e-scooter use. - 71% of respondents had used an e-scooter, 29% had not. - 25% of e-scooter users had used an e-scooter once, 75% had used e-scooters more than once. - Younger people, men, and those in full-time employment were most likely to use e-scooters. -First time e-scooter users were most motivated by wanting to have fun and try e-scootering. - Subsequent e-scooter use was increasingly motivated by practical considerations around the speed and convenience of e-scooters as a means of transport. - Respondents who had used an e-scooter more than once commonly reported using an e-scooter to travel to work, social engagements, or to shops or supermarkets. - Most people who had not used an e-scooter had not wanted or needed to use one. - Concerns about safety, expense, and not being able to wear normal clothes while e-scootering topped the list of practical reasons for not using an e-scooter. - For those who had used an e-scooter more than once: - 57% of e-scooter trips replaced trips that would otherwise have been made by active or sustainable modes (on foot, by bicycle, skateboard, or e-bike). - 28% of e-scooter trips replaced a trip by private car or van, motorcycle, ride source vehicle, or taxi. - 7% of e-scooter trips were new trips that would not otherwise have been made. - Over 90% of e-scooter users had ridden on the footpath, only around half (51%) of users and far fewer (26%) non-users think that the footpath is an appropriate environment to ride an e-scooter. - These results can help to generate better understandings of e-scooter use and can support the development of transport systems with benefits for New Zealand. Report available here: https://ir.canterbury.ac.nz/handle/10092/16336
Article
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Importance Since September 2017, standing electric scooters have proliferated rapidly as an inexpensive, easy mode of transportation. Although there are regulations for safe riding established by both electric scooter companies and local governments, public common use practices and the incidence and types of injuries associated with these standing electric scooters are unknown. Objective To characterize injuries associated with standing electric scooter use, the clinical outcomes of injured patients, and common use practices in the first US metropolitan area to experience adoption of this technology. Design, Setting, and Participants This study of a case series used retrospective cohort medical record review of all patients presenting with injuries associated with standing electric scooter use between September 1, 2017, and August 31, 2018, at 2 urban emergency departments associated with an academic medical center in Southern California. All electric scooter riders at selected public intersections in the community surrounding the 2 hospitals were also observed during a 7-hour observation period in September 2018. Main Outcomes and Measures Incidence and characteristics of injuries and observation of riders’ common use practices. Results Two hundred forty-nine patients (145 [58.2%] male; mean [SD] age, 33.7 [15.3] years) presented to the emergency department with injuries associated with standing electric scooter use during the study period. Two hundred twenty-eight (91.6%) were injured as riders and 21 (8.4%) as nonriders. Twenty-seven patients were younger than 18 years (10.8%). Ten riders (4.4%) were documented as having worn a helmet, and 12 patients (4.8%) had either a blood alcohol level greater than 0.05% or were perceived to be intoxicated by a physician. Frequent injuries included fractures (79 [31.7%]), head injury (100 [40.2%]), and contusions, sprains, and lacerations without fracture or head injury (69 [27.7%]). The majority of patients (234 [94.0%]) were discharged home from the emergency department; of the 15 admitted patients, 2 had severe injuries and were admitted to the intensive care unit. Among 193 observed electric scooter riders in the local community in September 2018, 182 (94.3%) were not wearing a helmet. Conclusions and Relevance Injuries associated with standing electric scooter use are a new phenomenon and vary in severity. In this study, helmet use was low and a significant subset of injuries occurred in patients younger than 18 years, the minimum age permitted by private scooter company regulations. These findings may inform public policy regarding standing electric scooter use.
Conference Paper
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This research investigates real time tilting measurement using Micro-Electro-Mechanical-system (MEMS) based inertial measurement unit (IMU). Accelerometers suffer from errors caused by external accelerations that sums to gravity and make accelerometers based tilting sensing unreliable and inaccurate. Gyroscopes can offset such drawbacks but have data drifting problems. This paper presents a study on complementary and Kalman filter for tilting measurement using MEMS based IMU. The complementary filter algorithm uses low-pass filter and high-pass filter to deal with the data from accelerometer and gyroscope while Kalman filter takes the tilting angle and gyroscope bias as system states, combining the angle derived from the accelerometer to estimate the tilting angle. The study carried out both static and dynamic experiments. The results showed that both Complementary and Kalman filter were less sensitive to variations and almost no signal coupling phenomenon and able to obtain smooth and accurate results.
Book
Christopher Kober beschreibt eine Methode, realistischen deutschen Autobahnverkehr zu simulieren. Dadurch kann er Fahrerassistenz- und automatisierte Funktionen umfangreicher als bisher in der Simulation und im Labor testen. Der Verkehrsfluss entsteht durch die Interaktion von Fahrerverhaltensmodellen, die er anhand statistischer Untersuchungen parametriert. Den Bezug zu realem Verkehrsfluss stellt der Autor über zwei Fahrsimulatorstudien sowie über die Auswertung von Zählstellendaten her. Der Inhalt • Fahrerverhaltensmodell • Verkehrsgenerierung • Kalibrierung und Validierung von Verkehrsfluss Die Zielgruppen • Dozierende und Studierende des Fachgebietes Simulationstechnik • Entwicklungsingenieure Automotive und Verkehrsforscher Der Autor Christopher Kober arbeitet derzeit im Bereich E/E Testing Prüfstand bei einem deutschen Automobilhersteller. Dort ist er themenverantwortlich für die „Digitale Erprobungsfahrt“, die sich zum Ziel gesetzt hat, eine reale Erprobung durch komplexe Simulation nachzubilden, um E/E-Komponenten im Labor zu testen. Er promovierte berufsbegleitend am Institut für Verbrennungsmotoren und Kraftfahrwesen (IVK) der Universität Stuttgart.
Article
Background: The introduction of shared electric scooters (e-scooters) to New Zealand has resulted in a large number of injuries. Within the past year, there have been studies addressing some of the impact of these e-scooter injuries, but none have included outpatient data or total regional costs. Methods: This was a retrospective review of e-scooter associated injuries presenting to Auckland region healthcare providers in the seven months since their introduction using Accident Compensation Corporation (ACC) Claims data. The type of injuries and key metrics of their overall hospital burden were assessed between September 2018 and April 2019. The financial cost of these injuries was also estimated. Results: A total of 770 patient presentations associated with e-scooters were identified during the study period. Of these, 524 (68.1%) were treated in the community by primary care physicians and 246 (31.9%) were treated in Auckland hospitals. The 246 hospital presentations used a total of 5,569 hospital bed-hours with 75 patients (30.5%) requiring admission and inpatient care. Of the hospital presentations, 49 patients (19.9%) required at least one operation, and 105 (42.7%) required specialist follow up care. 26.8% of injuries were thought to be associated with alcohol use. The estimated injury rate was 60 per 100,000 trips and hospital presentation rate was 20 per 100,000 trips. The combined cost attributable to these injuries was 608,843(NZD)forAucklandCityHospitaland608,843 (NZD) for Auckland City Hospital and 1,303,155 for the whole Auckland region. Conclusions: The overall burden of care due to the introduction of e-scooters to New Zealand has had significant impact both on the primary urban trauma center as well as community care facilities. E-scooter related injuries have had a large impact on regional healthcare costs.
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This report presents a novel orientation filter applicable to IMUs consisting of tri-axis gyroscopes and accelerometers, and MARG sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion and gyroscope bias drift compensation. The filter uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient-descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. The benefits of the filter include: (1) computationally inexpensive; requiring 109 (IMU) or 277 (MARG) scalar arithmetic operations each filter update, (2) effective at low sampling rates; e.g. 10 Hz, and (3) contains 1 (IMU) or 2 (MARG) adjustable parameters defined by observable system characteristics. Performance was evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. A simple calibration method is presented for the use of the optical measurement equipment in this application. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the filter achieves levels of accuracy exceeding that of the Kalman-based algorithm; < 0.6 • static RMS error, < 0.8 • dynamic RMS error. The implications of the low computational load and ability to operate at low sampling rates open new opportunities for the use of IMU and MARG sensor arrays in real-time applications of limited power or processing resources or applications that demand extremely high sampling rates.
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Steering a motorcycle or bicycle is counterintuitive; to turn right, you must steer left initially, and vice versa. You can execute this initially counter-directed turn by turning the handlebars explicitly (called counter-steering) or by throwing your hips to the side. Contrary to common belief, gyroscopic forces play only a limited role in balancing and steering [D. E. H. Jones, Phys. Today 23 (4), 34-40 (1970)].
Television presenter Emily Hartridge dies in electric scooter crash
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Capabilities and limitations related to vehicle dynamics and operator kinematics of electric scooter micro-mobility modes
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Micromobility's 15,000-mile checkup
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16-Year-Old E-Scooter Rider Dies in Collision With Tow Truck in New Jersey
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Man killed while riding e-scooter on French motorwayFatal e-scooter accident emerges just as California legalizes riding without a helmet
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Road Safety: Vision Zero on the move
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Autoliv performs first crash test of an e-scooter airbag
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BMW Motorrad präsentiert den neuen X2City
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