Master thesis: Reckless Riding Detection for Safer Micromobility

How can we increase safety by detecting reckless riding?

In my Master Thesis Project you will get the chance to work with real-world micromobility data from Voi’s operational fleet and contribute to next-generation rider safety systems with real deployment potential.

Marco Capuccini

Senior Machine Learning Engineer

The thesis

E-scooter safety has become a key challenge as shared micromobility expands across cities. While most riders behave responsibly, a small share engage in reckless behaviors—such as swerving, tandem riding, or single-handed riding—that significantly increase crash risk, as shown by Pai and Dozza (2025). Building on this foundation, Capuccini, Pai, and Carlsson (2025) introduced a testing-by-betting approach for detecting anomalies in ride data, providing a statistically grounded and computationally efficient method for onboard event detection. However, current detection methods still struggle to capture subtle, context-dependent riding patterns. This thesis aims to address these limitations by integrating richer sensing and learning-based models for more accurate and robust detection of reckless riding.

YOUR MISSION

The goal of this thesis is to advance reckless riding detection by leveraging rich sensor data and modern hypothesis testing and learning techniques. Possible directions include:

Integrating sensor data, such as accelerometer and gyroscope, to improve detection accuracy and robustness.
Exploring machine learning techniques such as contrastive learning–based sensor representations, LLM-based zero/few-shot detection, and statistical methods like testing-by-betting for theory-grounded guarantees.
Utilizing forward-facing camera data available on a subset of Voi’s fleet to complement sensor-based detection, using privacy-preserving methods.
Evaluating model performance under realistic deployment conditions, focusing on accuracy, computational efficiency, and robustness.

The exact focus will be shaped according to the student’s interests and background.

STUDENT PROFILE

We are looking for students that have:

Background in statistics, signal processing, or machine learning.
Programming skills in Python; familiarity with cloud computing platforms is beneficial.
Interest in applying data-driven methods to urban mobility and safety.
The project scope will be adapted to the student’s skills, experience, and interest.

About Voi Technology

Since 2018, Voi has been on a mission to create safe, sustainable and reliable micromobility for everyone. Today we are the leading shared micromobility operator in Europe, with HQ in Stockholm (Sweden), operating 150,000+ vehicles in 110+ towns and cities across 12 countries, serving 8+ million riders with over 400+ MILLION rides to date, and we have no intention of stopping here! We believe that micromobility will play a central role in changing how people move in our towns and cities in the future and want to ensure that the micromobility transformation happens the right way - through real innovative technology, partnership and transparent dialogue with towns, cities and governments, and by adapting our products to the local needs. In addition to improving quality of life in cities around the world, we are contributing to the solution for climate change by making sustainable transportation options more widely available and promoting renewable energy use and circularity along our supply chain.

Diversity Matters

As a Voiager, we offer you a friendly, collaborative and fast-paced workplace where you can bring your whole self to work, regardless of gender, age, ethnicity, religion, disability or sexual orientation - and welcome applications from whoever you are, including veterans, those actively serving as a Reservist and their partners. What is most important to us is how you treat others and what skills and value you bring to our team. We believe that having a wide diversity of people with different backgrounds and perspectives within Voi is essential to our success in delivering the best user experience to our equally diverse user base. With employees from over 50 nationalities working within Voi, we believe we have made good progress but know we can still do more - therefore we discourage any photos, personal letters, or disclosure of any information that concerns other information than your professional experience.

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