Analysis of E-Scooter Crash Severity Using Interpretable Machine Learning Approaches

Description

E-scooters have become an integral part of urban mobility systems in large metropolitan areas. Understanding the determinants of e-scooter crash severity is therefore crucial for improving road safety, guiding infrastructure design, and supporting evidence-based urban transport policies. However, crash severity is driven by complex and nonlinear interactions between user profiles, crash characteristics, and environmental conditions. Traditional statistical approaches often struggle to adequately capture this heterogeneity.

Supported by our AMI-QIM project, the objective of this internship is to analyzee-scooter crash severity in the Greater Paris area using interpretable machine learning approaches, such as gradient boosting algorithms (e.g., XGBoost or LightGBM) and SHapley Additive exPlanations (SHAP). The study will use the French road traffic accident database “Observatoire National Interministériel de la Sécurité Routière (ONISR)” for the...