Longitudinal and lateral performances of electrically assisted personal mobility vehicles: A comparative field test analysis of e-scooter, e-bicycle, and e-monowheel

• The study examines e-PMVs' braking, acceleration, and manoeuvring • E-bicycle outperforms e-scooter and e-monowheel in braking • Acceleration stays low for e-PMVs • There is little variation in lateral performance in lateral manoeuvres among e-PMVs • Data establish benchmarks for electric vehicles on dry roads The increasing popularity of Electrically Assisted Personal Mobility Vehicles (e-PMVs) represents a significant shift in urban transportation, offering flexibility and environmental benefits, yet raising safety concerns, particularly regarding accidents involving vulnerable road users. Research on the dynamic performance of these vehicles is essential to address such concerns and ensure safer interaction with other vehicles and ease their integration into urban traffic. This study explores the longitudinal (braking and acceleration) and lateral (avoidance and slalom) performances of e-PMVs through 127 controlled track tests conducted by three e-scooter riders, two e-bicycle riders, and one e-monowheel rider using one of the most common models of these vehicles. Specific performance indicators were defined for each maneuver to evaluate and compare dynamic capabilities. Results show that e-bicycles exhibits the most effective braking performance, achieving decelerations close to 6 m/s², while e-scooters and e-monowheels demonstrate lower deceleration rates around 3.7 m/s² and 2.8 m/s², respectively. Acceleration is quite similar across vehicles, ranging from 0.7 to 0.9 m/s² in sport/turbo mode. Performance differences during evasive maneuvers are minimal, with maximum roll rate ranging between 70 and 110°/s and maximum roll angle between 15° and 22°. In slalom, the e-monowheel shows the highest roll rate at 102°/s, followed by the e-bicycle at 87°/s and the e-scooter at 69°/s. The study highlights that e-PMVs, particularly e-scooters and e-monowheels, have substantially lower braking and acceleration capabilities than conventional motor vehicles, which likely increases the risk of traffic conflicts escalating into accidents. By filling a gap in the experimental knowledge base, especially concerning monowheels, these findings underscore the need for further research aimed at improving the design and safety features of e-PMVs, prioritizing improvements in braking efficiency and maneuverability to mitigate potential risks and offer users a safer mode of transportation.

Authors

Institutions

Publication Details

Journal
Transportation Engineering
Published
2025-10-28
DOI
https://doi.org/10.1016/j.treng.2025.100404
Primary Topic
Vehicle Dynamics and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Longitudinal and lateral performances of electrically assisted personal mobility vehicles: A comparative field test analysis of e-scooter, e-bicycle, and e-monowheel

Bastien Canu, Ebrahim Riahi, Claire Naude, Thierry Serre
Transportation Engineering
Vehicle Dynamics and Control Systems
article

Longitudinal and lateral performances of electrically assisted personal mobility vehicles: A comparative field test analysis of e-scooter, e-bicycle, and e-monowheel

Bastien Canu, Ebrahim Riahi, Claire Naude, Thierry Serre
article en

Abstract

• The study examines e-PMVs' braking, acceleration, and manoeuvring • E-bicycle outperforms e-scooter and e-monowheel in braking • Acceleration stays low for e-PMVs • There is little variation in lateral performance in lateral manoeuvres among e-PMVs • Data establish benchmarks for electric vehicles on dry roads The increasing popularity of Electrically Assisted Personal Mobility Vehicles (e-PMVs) represents a significant shift in urban transportation, offering flexibility and environmental benefits, yet raising safety concerns, particularly regarding accidents involving vulnerable road users. Research on the dynamic performance of these vehicles is essential to address such concerns and ensure safer interaction with other vehicles and ease their integration into urban traffic. This study explores the longitudinal (braking and acceleration) and lateral (avoidance and slalom) performances of e-PMVs through 127 controlled track tests conducted by three e-scooter riders, two e-bicycle riders, and one e-monowheel rider using one of the most common models of these vehicles. Specific performance indicators were defined for each maneuver to evaluate and compare dynamic capabilities. Results show that e-bicycles exhibits the most effective braking performance, achieving decelerations close to 6 m/s², while e-scooters and e-monowheels demonstrate lower deceleration rates around 3.7 m/s² and 2.8 m/s², respectively. Acceleration is quite similar across vehicles, ranging from 0.7 to 0.9 m/s² in sport/turbo mode. Performance differences during evasive maneuvers are minimal, with maximum roll rate ranging between 70 and 110°/s and maximum roll angle between 15° and 22°. In slalom, the e-monowheel shows the highest roll rate at 102°/s, followed by the e-bicycle at 87°/s and the e-scooter at 69°/s. The study highlights that e-PMVs, particularly e-scooters and e-monowheels, have substantially lower braking and acceleration capabilities than conventional motor vehicles, which likely increases the risk of traffic conflicts escalating into accidents. By filling a gap in the experimental knowledge base, especially concerning monowheels, these findings underscore the need for further research aimed at improving the design and safety features of e-PMVs, prioritizing improvements in braking efficiency and maneuverability to mitigate potential risks and offer users a safer mode of transportation.

Transportation EngineeringVol. 22
Université Gustave Eiffel (FR)
Agence Nationale de la Recherche
Openalex Percentile: Top 66%
Vehicle Dynamics and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.