Micro-Mobility Vehicle Dynamics and Rider Kinematics during Electric Scooter Riding

Garman, Christina; Como, Steven; Campbell, Ian C.; Wishart, Jeffrey; O’Brien, Kevin; McLean, Scott P

doi:10.4271/2020-01-0935

Cited by 31 publications

(11 citation statements)

References 12 publications

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“…However, a low rate (less than 6%) of helmet use by ES riders was reported and considered as an important factor contributing to severe head injuries and death in ES accidents (Iroz-Elardo and Currans, 2021;Lavoie-Gagne et al, 2021). For this reason, ES riders are highly recommended to wear a helmet in many countries, even though it is not yet compulsory (Garman et al, 2020;Serra et al, 2021). The standard bicycle helmets designed for low-speed riding are commonly used by ES riders (Haworth et al, 2021;Todd et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Head-ground impact conditions and helmet performance in E-scooter falls

Wei

Petit

Arnoux

et al. 2023

Accident Analysis & Prevention

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Section: Introductionmentioning

confidence: 99%

Head-ground impact conditions and helmet performance in E-scooter falls

Wei

Petit

Arnoux

et al. 2023

Accident Analysis & Prevention

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“…A research need for the braking systems of the many available shared e-scooter models has been identified (Garman et al, 2020), but braking systems of e-scooter models have not been researched in detail. During the time of this study, six shared e-scooter providers were active in Berlin: Bird, Circ, Jump, Lime, Tier, and VOI (Kraftfahrtbundesamt, 2019).…”

Section: Braking System Of E-scootersmentioning

confidence: 99%

Braking bad – Ergonomic design and implications for the safe use of shared E-scooters

et al. 2021

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“…While the literature is constantly evolving few studies have focused on vehicle and rider dynamics and performance. Garman et al 7 provided a characterization of rider and vehicle dynamics for electric scooter riding in naturalistic environments. JD Cano-Moreno et al 8 studied the vibrational effects of e-scooters on riders using a multibody dynamics approach however, they assumed the rider is rigidly connected to the e-scooter.…”

Section: Introductionmentioning

confidence: 99%

Analysis of an e-scooter and rider system dynamic response to curb traversing through physics-informed machine learning methods

Arslan¹,

Uyulan

2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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The use of big data to statistically evaluate system performance with the help of Machine Learning (ML) tools is a very popular method. Classic experimental methods of system performance can be impractical when needing to test a variety of input parameters within a range of specific limits and can take enormous time and effort. Mathematical models which numerically mimic the physical system dynamics can be deployed for the same aim. In this study, a system modeling based data analysis approach has been implemented to gather the data to understand the input-output relationship of an e-scooter and rider system in terms of its stability dynamics while driving over a curb of a specific height. Based on the test conditions specified in the German e-KFV standard, a virtual driving test has been created which consists of a kinematic e-scooter model with its rider and a test track. In the range of input parameter space, over 20,000 numbers of different cases have been solved and the results are recorded. The interaction between the rider and the vehicle is achieved through elastic connections which enable not only the recording of loading data on the arms and legs, but also allows the simulation to reflect the real-time effects of the rider scooter interaction. Through statistical analysis and using ML techniques, the influence of each parameter (tire size, speed, rider mass, etc.) on the objective outputs (loading on arms, ability to ascend the curb, etc.) regarding the stability and safety while driving over the curb have been reported and shared for further analysis by the e-scooter community.

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Micro-Mobility Vehicle Dynamics and Rider Kinematics during Electric Scooter Riding

Cited by 31 publications

References 12 publications

Head-ground impact conditions and helmet performance in E-scooter falls

Head-ground impact conditions and helmet performance in E-scooter falls

Braking bad – Ergonomic design and implications for the safe use of shared E-scooters

Analysis of an e-scooter and rider system dynamic response to curb traversing through physics-informed machine learning methods

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