An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction

TL;DR

This study develops and interprets machine learning models to predict traffic accident fatalities in Bangladesh, highlighting key risk factors and outperforming existing models with LightGBM for targeted safety interventions.

Contribution

Introduces an interpretable machine learning framework using SHAP for predicting traffic accident fatalities, with a focus on developing country data and model transparency.

Findings

LightGBM achieves ROC-AUC of 0.72

Casualty class and accident time are key factors

Model insights support targeted safety measures

Abstract

Road traffic accidents (RTA) pose a significant public health threat worldwide, leading to considerable loss of life and economic burdens. This is particularly acute in developing countries like Bangladesh. Building reliable models to forecast crash outcomes is crucial for implementing effective preventive measures. To aid in developing targeted safety interventions, this study presents a machine learning-based approach for classifying fatal and non-fatal road accident outcomes using data from the Dhaka metropolitan traffic crash database from 2017 to 2022. Our framework utilizes a range of machine learning classification algorithms, comprising Logistic Regression, Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, Gradient Boosting, LightGBM, and Artificial Neural Network. We prioritize model interpretability by employing the SHAP (SHapley Additive exPlanations) method, which elucidates the key factors influencing accident fatality. Our results demonstrate that LightGBM outperforms other models, achieving a ROC-AUC score of 0.72. The global, local, and feature dependency analyses are conducted to acquire deeper insights into the behavior of the model. SHAP analysis reveals that casualty class, time of accident, location, vehicle type, and road type play pivotal roles in determining fatality risk. These findings offer valuable insights for policymakers and road safety practitioners in developing countries, enabling the implementation of evidence-based strategies to reduce traffic crash fatalities.