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Dataset on personal mobility vehicle's regular riding and fall events.

Ramon Sanchez-Iborra¹, Luis Bernal-Escobedo¹, Jose Santa²

¹Department of Information and Communication Engineering, University of Murcia, Spain.

|June 13, 2025

Summary

This study introduces a new dataset from electric scooters to improve automatic accident detection. This data aids in developing systems for faster emergency response and safer urban mobility.

Keywords:

Falls Intelligent transportation systems Riding Scooter Urban mobility

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Area of Science:

Urban planning and transportation engineering
Data science and machine learning
Public safety and emergency response

Background:

Personal mobility vehicles (PMVs) like electric scooters are increasingly common in urban areas.
The integration of PMVs into smart cities presents challenges, including safety concerns and accident management.
Rapid detection of accidents involving PMVs is critical for timely emergency services intervention.

Purpose of the Study:

To present a novel dataset capturing sensor data from electric scooters during normal operation and various accident scenarios.
To facilitate the development of advanced algorithms for automatic detection and classification of riding situations and accidents.
To support research in enhancing the safety and integration of PMVs in smart city environments.

Main Methods:

Collection of sensor data (e.g., accelerometer, gyroscope) from an electric scooter.
Simulating and recording diverse accident types, including frontal collisions and lateral falls.
Organizing and annotating the collected data for use in machine learning models.

Main Results:

The dataset encompasses a variety of real-world riding conditions and accident scenarios.
Provides a comprehensive resource for training and validating accident detection algorithms.
Demonstrates the potential for sensor data to differentiate between normal and critical events.

Conclusions:

The developed dataset is a valuable resource for advancing the field of automatic accident detection for personal mobility vehicles.
Enabling faster response times can significantly improve safety outcomes for scooter and e-bike users.
This work contributes to the broader goal of creating safer and more efficient smart city transportation ecosystems.