Identifying Driving Behaviour Archetypes in Vehicle Emissions with SHAP Explanations

A. Mancuso, A. Iodice D'Enza, F. Palumbo, R. Simone

Telematics data allow large-scale assessment of how driving behaviour affects vehicle emissions, yet most estimation pipelines rely on complex machine learning models with limited interpretability. This study introduces an interpretable framework that combines emission reconstruction, surrogate modelling, and model-agnostic explanation techniques. SHapley Additive exPlanations are employed to break down trip-level emission estimates into individual feature contributions, while global summaries highlight the most influential behavioural patterns. In addition, Archetypal Analysis on SHAP values uncovers distinct emission-related driving profiles. This behavioural segmentation supports the design of targeted and scalable eco-driving strategies, enhancing both understanding and practical applicability of emission analysis. The proposed approach improves transparency and facilitates data-driven decision-making for sustainable mobility policies.

Palabras clave: Machine Learning, SHAP, Archetypal Analysis, Eco-driving, Telematics data

Aprendizaje Automático y Métodos Estadísticos
2 de septiembre de 2026  15:30
Aula 20

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