A data-driven supervised machine learning study for road-accident prediction in smart transportation operations and planning in smart cities

2 Methods The goal of this study is to design and evaluate a supervised-ML benchmark for binary road-accident occurrence prediction using the public Kaggle Transportation and Accident Prediction Dataset ().

FIGURE 1 2.1 Dataset The Transportation and Accident Prediction Dataset, a publicly accessible synthetic dataset obtained from Kaggle, was used in this study ().

A comparative analysis of hypertension risk detection using machine learning and deep learning techniques .

Exploratory machine learning and deep learning classifiers for improved AI asthma detection ,” in 2026 International Conference on Smart Multidomain Integrated Learning Environments (ICSMILE) .

10.1016/j.ress.2026.112230 Summary Keywords class imbalance , intelligent transportation systems , machine learning , Orange3 , random forest , road accident occurrence , smart cities Citation Arabiat A and Owida HA (2026) A data-driven supervised machine learning study for road-accident prediction in smart transportation operations and planning in smart cities .