This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Micro-Mobility Vehicle Dynamics and Rider Kinematics during Electric Scooter Riding
Technical Paper
2020-01-0935
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Micro-mobility is a fast-growing trend in the transportation industry with stand-up electric scooters (e-scooters) becoming increasingly popular in the United States. To date, there are over 350 ride-share e-scooter programs in the United States. As this popularity increases, so does the need to understand the performance capabilities of these vehicles and the associated operator kinematics. Scooter tip-over stability is characterized by the scooter geometry and controls and is maintained through operator inputs such as body position, interaction with the handlebars, and foot placement. In this study, testing was conducted using operators of varying sizes to document the capabilities and limitations of these e-scooters being introduced into the traffic ecosystem.
A test course was designed to simulate an urban environment including sidewalk and on-road sections requiring common maneuvers (e.g., turning, stopping points, etc.) for repeatable, controlled data collection. A commercially available e-scooter was instrumented to measure acceleration and velocity, steering angle, roll angle, and GPS position. Operators ranging from the 15th percentile to the 85th percentile were instrumented with wearable sensors to gain insight into the positions, velocities, and accelerations of the head, torso, and extremities. Additionally, load cells were mounted on the ride platform to provide data related to dynamic weight transfer. Analysis of video, wearable sensor data, and scooter instrumentation data provided insight into the vehicle dynamics and operator kinematics with varying operator anthropometry, contributing to discussion of the capabilities and limitations of this popular micro-mobility transportation mode.
Authors
Topic
Citation
Garman, C., Como, S., Campbell, I., Wishart, J. et al., "Micro-Mobility Vehicle Dynamics and Rider Kinematics during Electric Scooter Riding," SAE Technical Paper 2020-01-0935, 2020, https://doi.org/10.4271/2020-01-0935.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 | ||
| Unnamed Dataset 5 | ||
| Unnamed Dataset 6 | ||
| Unnamed Dataset 7 |
Also In
References
- Aizpuru , M. , Farley , K.X. et al. Motorized Scooter Injuries in the Era of Scooter-Shares: A Review of the National Electronic Surveillance System American Journal of Emergency Medicine 37 1133 1138 2019
- Austin Public Health 2019
- Badeau , A. , Carman , C. et al. Emergency Department Visits for Electric Scooter-Related Injuries after Introduction of an Urban Rental Program American Journal of Emergency Medicine 37 1531 1533 2019
- Bekhit , M.N.Z. , Le Fevre , J. , and Bergin , C.J. Regional Healthcare Costs and Burden of Injury Associated with Electric Scooters Injury 2019
- Chang , A.Y.J. , Miranda-Moreno , L. et al. 2019
- Dozza , M. , Piccinini , G.F.B. , and Werneke , J. Using Naturalistic Data to Assess e-Cyclist Behavior Transportation Research Part F 41 217 226 2016
- Du , W. , Yang , J. et al. Understanding On-Road Practices of Electric Bike Riders: An Observational Study in a Developed City of China Accident Analysis and Prevention 59 319 326 2013
- Hardt , C. and Bogenberger , K. Usage of e-Scooters in Urban Environments Transportation Research Procedia 37 155 162 2019
- Langford , B.C. , Chen , J. , and Cherry , C.R. Risky Riding: Naturalistic Methods Comparing Safety Behavior from Conventional Bicycle Riders and Electric Bike Riders Accident Analysis and Prevention 82 220 226 2015
- Madgwick , S.O.H. , Harrison , A.J.L. , and Vaidyanathan , R. Estimation of IMU and MARG Orientation Using a Gradient Descent Algorithm 2011 IEEE International Conference on Rehabilitation Robotics 2011 1 7
- Mathew , J.K. , Liu , M. et al. Analysis of e-Scooter Trips and Their Temporal Usage Patterns ITE Journal 89 6 44 49 2019
- Peebles , L. and Norris , B. ADULTDATA: The Handbook of Adult Anthropometric and Strength Measurements - Data for Design Safety Nottingham, UK Institute for Occupational Ergonomics, Department of Manufacturing Engineering and Operations Management, University of Nottingham 1998
- Todd , J. , Krauss , D. , Zimmermann , J. , and Dunning , A. Behavior of Electric Scooter Operators in Naturalistic Environments SAE Technical Paper 2019-01-1007 2019 https://doi.org/10.4271/2019-01-1007
- Trivedi , B. , Kesterke , M.J. et al. Craniofacial Injuries Seen with the Introduction of Bicycle-Share Electric Scooters in an Urban Setting J Oral Maxillofac Surg 77 2292 2297 2019
- Trivedi , T.K. , Liu , C. et al. Injuries Associated with Standing Electric Scooter Use JAMA Network Open 2 1 e187381 2019
- Warner , C.Y. , Smith , G.C. , James , M. , and Germane , G. Friction Applications in Accident Reconstruction SAE Technical Paper 830612 1983 https://doi.org/10.4271/830612