# Naturalistic Driving Study to assess e-scooter and e-bicycle travel speeds

Claire Naude, Ebrahim Riahi, Bastien Canu, Thierry Serre

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Claire Naude, Ebrahim Riahi, Bastien Canu, Thierry Serre. Naturalistic Driving Study to assess e-scooter and e-bicycle travel speeds. International Co-operation on Theories and Concepts in Traffic Safety (ICTCT), Oct 2024, THE HAGUE, Netherlands. hal-05021226

HAL Id: hal-05021226

https://univ-eiffel.hal.science/hal-05021226v1

Submitted on 4 Apr 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# NATURALISTIC DRIVING STUDY
## TO ASSESS E-SCOOTER AND E-BICYCLE TRAVEL SPEEDS

Claire Naude, Ebrahim Riahi, Bastien Canu, Thierry Serre

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36th ICTCT conference
The Hague, Netherlands
17–18 October

## Introduction

The shift towards sustainable urban mobility has introduced new forms of transport like e-bicycles and e-scooters, altering travel speeds and increasing safety risks. As speed is a key factor in accidents, understanding how these vehicles are used is essential. To address this, a Naturalistic Driving Study was conducted to collect real-world data on e-scooter and e-bicycle usage, offering valuable insights into user behaviours and speeds.

## Material and Methods

121 users (56 e-scooter and 65 e-bicycle users) were tracked over two months in three French regions: South (Marseille, Oct-Nov 2022), East (Lyon, May-June 2023), and Centre (Paris, Nov-Dec 2023). Smartphones mounted on handlebars recorded GPS speed and position at 1-s intervals (along with other metrics). The data provided average distances and speeds per user, along with an analysis of high speeds and those exceeding the 25 km/h speed limit in France.

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## Results

### Average data

A total of 6,800 trips were recorded, covering over 26,000 km and 1,600 hours. E-scooter trips averaged 9.8 minutes and 2.5 km, while e-bicycle trips averaged 19 minutes and 5.3 km. The average speeds were closer: 14.6 km/h for e-scooters and 15.7 km/h for e-bicycles.

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The average speed distributions show close alignment between average and median values, suggesting symmetrical distributions with few extreme values. Speeds were calculated without stops, but actual traffic speeds are underestimated by 0.7 km/h for e-scooters and 0.8 km/h for e-bicycles.

### High speeds

Electric Personal Mobility Devices, such as e-scooters and unregistered e-bikes, are limited to 25 km/h on public roads in France, with integrated speed limiters to ensure compliance. Speed data reveals that e-scooter users exceed this limit 11% of the time, and e-bicycle users 13%. While averages are low, behaviour varies widely, with some users frequently exceeding the limit—up to 61% of the time on e-scooters and 48% on e-bikes. Maximum recorded speeds reach 50 km/h for e-scooters and 44 km/h for e-bikes. The fleet includes 43 approved e-scooter models and 11 modified ones, capable of 30-80 km/h, while all e-bikes are reported as unmodified.

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## contact

claire.naude@univ-eiffel.fr

## Bibliography

Almannaa, M. H., Ashqar, H. I., Elhenawy, M., Masoud, M., Rakotonirainy, A., Rakha, H. 2021. A comparative analysis of e-scooter and e-bike usage patterns: Findings from the City of Austin, TX. International Journal of Sustainable Transportation, 15(7), 571–579.

Dozza, M., Bianchi Piccinini, G. F., Werneke, J. 2016. Using naturalistic data to assess e-cyclist behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 41, 217–226.

## Conclusion

Dozza et al. (2015) conducted a study in Gothenburg, finding an average e-bicycle speed of 16.9 km/h, which is slightly higher than the 15.7 km/h recorded in this study. Almannaa et al. (2021) reported average speeds in Austin ranging from 10.8 to 12.4 km/h for e-bicycles and from 7.9 to 10 km/h for e-scooters, both significantly lower than our findings. Despite the 25 km/h speed limit for electric personal mobility devices in France, users frequently exceed this limit, with some reaching 50 km/h on e-scooters and 44 km/h on e-bicycles. This study aims to inform public policy and will be complemented by a thorough evaluation of risky situations based on comprehensive real-world data.

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