Communications - Scientific letters of the University of Zilina X:X | DOI: 10.26552/com.C.2026.039

Optuna-Optimized Machine Learning for Traffic Accident Severity Prediction in Jordan under Class Imbalance Conditions

Maen Qaseem Ghadi ORCID...

Al-Zaytoonah University of Jordan, Department of Civil and Infrastructure Engineering, Amman, Jordan

A machine learning framework for predicting the traffic accident severity under class imbalance conditions is predented in Amman, Jordan. The methodology began with a preprocessing pipeline consisting of IQR-based (Interquartile range) outlier removal, label encoding, feature scaling, and Synthetic Minority Over-sampling Technique (SMOTE). Sixteen model configurations were considered, comprising four ensemble learning algorithms (XGBoost, LightGBM, Random Forest, CatBoost) under oversampling, with Optuna-based Bayesian hyperparameter optimisation. The result indicated that the class balancing using SMOTE had a greater influence on the predictive performance than hyperparameter optimisation, specifically for CatBoost model. Feature importance showed that vehicles involved, collision type, and speed are the strongest predictors of accident severity.

Keywords: hyperparameter tuning, SMOTE class imbalance, traffic accident severity, machine learning, Optuna

Grants and funding:

The authors received no financial support for the research, authorship and/or publication of this article.

Conflicts of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: April 19, 2026; Accepted: June 15, 2026; Prepublished online: July 3, 2026