Traffic Accident Analysis and Prediction Using Machine Learning Models in Türkiye from 2021 to 2024

Authors : Md Al Amin Hossain, Humar Kahramanli Örnek, Tahir Sag

Pages : 354-379

Doi:10.33484/sinopfbd.1659592

View : 121 | Download : 380

Publication Date : 2025-12-24

Article Type : Research Paper

Abstract :Traffic accidents represent a major challenge to public safety and urban development. In recent years, the number of road traffic accidents has been increasing due to the rising global population and the growing number of vehicles, leading to numerous fatalities and injuries. This study examines traffic accidents in five major cities of Türkiye from 2021 to 2024, aiming to identify trends and predict future accidents using linear regression and random forest regressor models. Data for this analysis were obtained from the Gendarmerie General Command of the Ministry of Internal Affairs, Republic of Türkiye. To evaluate model performance, key metrics such as Mean Absolute Error, Mean Squared Error, and R-squared were utilized. The results indicate significant variations in accident patterns across cities, months, and years. Furthermore, findings highlight the effectiveness of machine learning models in predicting traffic incidents with high accuracy. Among the two models, the random forest regressor outperforms linear regression in terms of evaluation metrics. Moreover, the analytical results indicate an upward trend in accidents, fatalities, and injuries across the five cities, particularly in Ankara and İzmir. These predictive and analytical insights can provide valuable guidance for policymakers and researchers in formulating effective strategies to mitigate traffic accidents and enhance road safety.
Keywords : Doğrusal regresyon, Makine öğrenimi, Rastgele Orman, Trafik kazası

ORIGINAL ARTICLE URL