Küresel Yol Güvenliğini Geliştirmek İçin Makine Öğreniminden Yararlanma: Kapsamlı Bir İnceleme

Year 2024, Volume: 27 Issue: 6, 2127 - 2137

Selma Bulut

https://doi.org/10.2339/politeknik.1348075

Abstract

Küresel kentleşme hızlanırken, yol güvenliği, artan trafik kazaları ve ölümlerin altını çizdiği acil bir endişe olmaya devam etmektedir. Karayolu Trafik Yaralanmaları (RTI), dünya çapında sekizinci önde gelen ölüm nedeni haline geldi. Makale, trafik kazalarını, bunların ciddiyetini ve nedensel faktörleri tahmin etmede makine öğreniminin potansiyelini derinlemesine araştırmaktadır. Bu çalışma, Addis Ababa Şehri Polis Departmanından alınan trafik kazası kayıtları üzerindeki makine öğrenimi modellerini kapsamlı bir şekilde değerlendirmektedir. 15 özelliğe sahip 12.316 kayıttan oluşan veri setinde, Sentetik Azınlık Aşırı Örnekleme Tekniği (SMOTE) ve Min-Max ölçekleme başta olmak üzere ön işleme teknikleri uygulanmıştır. Beş algoritma – Random Forest (RF), Gaussian Naive Bayes, CatBoostClassifier, LightGBM ve XGBoost – tahmin doğruluğu açısından test edilmiştir. Bulgular, SMOTE ve Min-Max uygulamasından sonra %92,2'lik bir tepe doğruluğu elde eden RF modelinin hakimiyetine ışık tutmaktadır. Mevcut literatürle karşılaştırmalı bir analiz, RF'nin çeşitli veri kümelerinde yinelenen etkili bir model olmasına rağmen, veri ön işlemenin ve belirli veri kümelerine model uygunluğunun öneminin çok önemli olduğunu göstermiştir. Bu çalışma, trafik kazası analizinde makine öğreniminin potansiyelinin ve araştırmacıların optimum sonuçlar için yapması gereken incelikli seçimlerin altını çizmektedir.

Keywords

Trafik Kazası Analizi, Makine Öğrenmesi, Rastgele Orman, Min-Maks Ölçeklendirme, Karşılaştırmalı Çalışma.

Harnessing Machine Learning to Enhance Global Road Safety: A Comprehensive Review

Abstract

As global urbanization accelerates, road safety remains a pressing concern, underscored by escalating traffic accidents and fatalities. Road Traffic Injuries (RTI) have become the eighth leading cause of death worldwide. The article delves deep into the potential of machine learning in predicting traffic accidents, their severity, and causal factors. This study comprehensively evaluates machine learning models on traffic accident records sourced from the Addis Ababa City Police Department. Comprising 12,316 records with 15 features, the dataset underwent preprocessing techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Min-Max scaling. Five algorithms – Random Forest (RF), Gaussian Naive Bayes, CatBoostClassifier, LightGBM, and XGBoost – were tested for their prediction accuracy. The findings spotlight the dominance of the RF model, achieving a peak accuracy of 92.2% post-SMOTE and Min-Max application. A comparative analysis with existing literature showed that while RF is a recurrently effective model across various datasets, data preprocessing and model suitability to specific datasets is paramount. This study underscores the potential of machine learning in traffic accident analysis and the nuanced choices researchers must make for optimal outcomes.

Keywords

Traffic Accident Analysis, Machine Learning, Random Forest, Min-Max Scaling, Comparative Study.

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