Testing by Betting for Anomaly Detection in Rental E-Scooter GNSS Traces

Marco Capuccini, Rahul Rajendra Pai, Lars Carlsson
Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 266:633-644, 2025.

Abstract

Shared micromobility, particularly rental e-scooters, has rapidly transformed urban transportation. Voi Technology has been at the forefront of this shift, powering over 300M rides across Europe. While most customers use the service responsibly, mitigating reckless riding emerges as a significant challenge given the high ridership. Previous research shows that riders taking indirect routes are more likely to be involved in safety-critical events, suggesting potentially irresponsible riding behavior. However, directness alone can overlook important intra-trip patterns. Therefore, in this study, we use GNSS positioning as a proxy for intra-trip riding behavior. We model a typical ride as a sequence of turning angles derived from GNSS coordinates and detect anomalies leveraging the testing-by-betting framework, which provides formal guarantees on false positive rates while achieving a favorable trade-off with false negatives. The presented method is designed to operate under limited onboard compute, with minimal complexity for deployment across large vehicle fleets, without requiring the GNSS trace to be stored—a key privacy advantage. In a real-world evaluation, the method detects approximately 60% of reckless rides while maintaining operationally acceptable false positive rates.

Cite this Paper

BibTeX
@InProceedings{pmlr-v266-capuccini25a, title = {Testing by Betting for Anomaly Detection in Rental E-Scooter GNSS Traces}, author = {Capuccini, Marco and Pai, Rahul Rajendra and Carlsson, Lars}, booktitle = {Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {633--644}, year = {2025}, editor = {Nguyen, Khuong An and Luo, Zhiyuan and Papadopoulos, Harris and Löfström, Tuwe and Carlsson, Lars and Boström, Henrik}, volume = {266}, series = {Proceedings of Machine Learning Research}, month = {10--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v266/main/assets/capuccini25a/capuccini25a.pdf}, url = {https://proceedings.mlr.press/v266/capuccini25a.html}, abstract = {Shared micromobility, particularly rental e-scooters, has rapidly transformed urban transportation. Voi Technology has been at the forefront of this shift, powering over 300M rides across Europe. While most customers use the service responsibly, mitigating reckless riding emerges as a significant challenge given the high ridership. Previous research shows that riders taking indirect routes are more likely to be involved in safety-critical events, suggesting potentially irresponsible riding behavior. However, directness alone can overlook important intra-trip patterns. Therefore, in this study, we use GNSS positioning as a proxy for intra-trip riding behavior. We model a typical ride as a sequence of turning angles derived from GNSS coordinates and detect anomalies leveraging the testing-by-betting framework, which provides formal guarantees on false positive rates while achieving a favorable trade-off with false negatives. The presented method is designed to operate under limited onboard compute, with minimal complexity for deployment across large vehicle fleets, without requiring the GNSS trace to be stored—a key privacy advantage. In a real-world evaluation, the method detects approximately 60% of reckless rides while maintaining operationally acceptable false positive rates.} }
Endnote
%0 Conference Paper %T Testing by Betting for Anomaly Detection in Rental E-Scooter GNSS Traces %A Marco Capuccini %A Rahul Rajendra Pai %A Lars Carlsson %B Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2025 %E Khuong An Nguyen %E Zhiyuan Luo %E Harris Papadopoulos %E Tuwe Löfström %E Lars Carlsson %E Henrik Boström %F pmlr-v266-capuccini25a %I PMLR %P 633--644 %U https://proceedings.mlr.press/v266/capuccini25a.html %V 266 %X Shared micromobility, particularly rental e-scooters, has rapidly transformed urban transportation. Voi Technology has been at the forefront of this shift, powering over 300M rides across Europe. While most customers use the service responsibly, mitigating reckless riding emerges as a significant challenge given the high ridership. Previous research shows that riders taking indirect routes are more likely to be involved in safety-critical events, suggesting potentially irresponsible riding behavior. However, directness alone can overlook important intra-trip patterns. Therefore, in this study, we use GNSS positioning as a proxy for intra-trip riding behavior. We model a typical ride as a sequence of turning angles derived from GNSS coordinates and detect anomalies leveraging the testing-by-betting framework, which provides formal guarantees on false positive rates while achieving a favorable trade-off with false negatives. The presented method is designed to operate under limited onboard compute, with minimal complexity for deployment across large vehicle fleets, without requiring the GNSS trace to be stored—a key privacy advantage. In a real-world evaluation, the method detects approximately 60% of reckless rides while maintaining operationally acceptable false positive rates.
APA
Capuccini, M., Pai, R.R. & Carlsson, L.. (2025). Testing by Betting for Anomaly Detection in Rental E-Scooter GNSS Traces. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 266:633-644 Available from https://proceedings.mlr.press/v266/capuccini25a.html.

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