Machine Learning for Liability Attribution in Pedestrians Involved in Traffic Crashes: Interpretability and Class Imbalance Solutions

This paper proposes a Machine Learning (ML) framework designed to attribute liability between drivers and pedestrians in traffic crashes. This study applies classification algorithms and interpretability techniques to analyze judicial rulings related to pedestrian crashes in Badajoz, Spain, from 201...

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Bibliografische Detailangaben

Hauptverfasser:	Felisa C. Gragera-Peña, Miguel A. Jaramillo-Morán, Alejandro Moreno-Sanfélix
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	MDPI AG 2026-07-01
Schriftenreihe:	Mathematics
Schlagworte:	class imbalance explainable AI pedestrian crashes urban road safety
Online-Zugang:	https://www.mdpi.com/2227-7390/14/13/2389
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