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 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.