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 2015 to 2024. The primary objective is to identify recurring crash patterns and determine liability levels for the parties involved. Several classification algorithms were evaluated, including Support Vector Machines (SVM), Neural Network (NN), Decision Trees (DT), Boosted Trees (BT), Naïve Bayes (NB), Random Forest (RF), K-Nearest Neighbors (K-NN), and Logistic Regression (LR). Among them, the quadratic-kernel SVM achieved the highest overall performance. To address the severe class imbalance of the data, stratified k-fold cross-validation and the Synthetic Minority Oversampling Technique (SMOTE) were applied to enhance the robustness and generalization capability of the model. A multiclass classification framework was implemented, and SHAP (SHapley Additive exPlanations) was integrated to improve interpretability by quantifying the contribution of each feature to the model’s predictions. The analysis identified critical factors that play a significant role in determining liability outcomes: driver license status, crash location, lighting conditions, reaction time, and the presence of drugs or alcohol. This research aims to contribute to the legal domain. While most existing studies have focused on predicting injury severity, few have addressed liability attribution. This is a multifactorial task that requires a comprehensive analysis of judicial decisions. The results demonstrate that machine learning-driven liability attribution can support judicial decision-making and provide valuable insights for the development of proactive urban traffic safety strategies.
Gragera-Peña et al. (Fri,) studied this question.