Predicting Segment-Level Road Traffic Injury Counts Using Machine Learning Models: A Data-Driven Analysis of Geometric Design and Traffic Flow Factors

Accurate prediction of road traffic crash severity is essential for developing data-driven safety strategies and optimizing resource allocation. This study presents a predictive modeling framework that utilizes Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN) to estimate segment-level frequencies of fatalities, serious injuries, and slight injuries on Hungarian roadways. The model integrates an extensive array of predictor variables, including roadway geometric design features, traffic volumes, and traffic composition metrics. To address class imbalance, each severity class was modeled using resampled datasets generated via the Synthetic Minority Over-sampling Technique (SMOTE), and model performance was optimized through grid-search cross-validation for hyperparameter optimization. For the prediction of serious- and slight-injury crash counts, the Random Forest (RF) ensemble model demonstrated the most robust performance, consistently attaining test accuracies above 0.91 and coefficient of determination (R²) values exceeding 0.95. In contrast, for fatalities count prediction, the Gradient Boosting (GB) model achieved the highest accuracy (0.95), with an R² value greater than 0.87. Feature importance analysis revealed that heavy vehicle flows consistently dominate crash severity prediction. Horizontal alignment features primarily influenced fatal crashes, while capacity utilization was more relevant for slight and serious injuries, reflecting the roles of geometric design and operational conditions in shaping crash occurrence and severity. The proposed framework demonstrates the effectiveness of machine learning approaches in capturing non-linear relationships within transportation safety data and offers a scalable, interpretable tool to support evidence-based decision-making for targeted safety interventions.