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

Selma Bulut

Makale detayları

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

Yazarlar:

Selma Bulut

Yayıncı:

Gazi University

Ana Başlıklar:

Yarı ve Denetimsiz Öğrenme | Yapay Zeka (Diğer)

Dergi:

Politeknik Dergisi Vol.27 Issue 6, pp.2127-2137

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001333511

pISSN:

2147-9429

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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 92,2lik bir tepe doğruluğu elde eden RF modelinin hakimiyetine ışık tutmaktadır. Mevcut literatürle karşılaştırmalı bir analiz, RFnin 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.

Makale detayları

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

Yazarlar:

Selma Bulut

Yayıncı:

Gazi University

Ana Başlıklar:

Yarı ve Denetimsiz Öğrenme | Yapay Zeka (Diğer)

Dergi:

Politeknik Dergisi Vol.27 Issue 6, pp.2127-2137

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001333511

pISSN:

2147-9429

Traffic Accident Analysis

Machine Learning

Random Forest

Min-Max Scaling

Comparative Study.Trafik Kazası Analizi

Makine Öğrenmesi

Rastgele Orman

Min-Maks Ölçeklendirme

Karşılaştırmalı Çalışma.

Sayı

Makale detayları

Yazarlar: Selma Bulut

Yayıncı: Gazi University

Ana Başlıklar: Yarı ve Denetimsiz Öğrenme | Yapay Zeka (Diğer)

Dergi: Politeknik Dergisi Vol.27 Issue 6, pp.2127-2137

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001333511

pISSN: 2147-9429

Sayı

Yazarlar:

Selma Bulut

Yayıncı:

Gazi University

Ana Başlıklar:

Yarı ve Denetimsiz Öğrenme | Yapay Zeka (Diğer)

Dergi:

Politeknik Dergisi Vol.27 Issue 6, pp.2127-2137

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001333511

pISSN:

2147-9429