MAKİNE ÖĞRENMESİ ALGORİTMALARI KULLANILARAK TRAFİK KAZASI ŞİDDETİNİN TAHMİNİ ÜZERİNE BİR ÇALIŞMA

Year 2023, Volume: 7 Issue: 2, 152 - 161, 29.12.2023

Gül Fatma Türker Fatih Kürşad Gündüz

Abstract

Trafik kazalarının şiddeti insan hayatını etkilemesi açısından büyük önem arz etmektedir. Trafik kazası şiddetinin tahmin edilmesi, trafik kazalarının önlenmesi ve güvenli bir sürüş sağlanması için önemlidir. Tahmin yöntemlerinin başarısı sayesinde ilgili risk faktörleri çıkarılmakta ve karşı önlemler alınabilmektedir. Trafik kazalarının sebepleri çok fazla değişkene bağlı olabilmektedir. Özellikle görünür sebepler olarak yol bozuklukları, hava şartları, araç hızı gibi faktörler trafik kazalarına neden olmaktadır. Oluşan bir kazada kazanın anlık durumunun ne olduğu bilgisini almak oldukça güçtür. Ancak sebeplerden kaynaklanan bir olası kazanın şiddeti hesaplanarak gelecek zamanda oluşacak kazaların önüne geçilmesi mümkündür. Bu çalışmada kaza şiddeti tahmini için ABD eyaletlerinde 2016-2023 yılları arasında toplanan trafik kaza veri seti üzerinde makine öğrenmesi algoritmaları test edilerek kaza şiddeti tahminleri yapılmıştır. Kazaya sebep olan etkenleri belirlemek için makine öğrenme tekniklerinden Decision Tree, Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN) ve Navie Bayes (NB) algoritmaları kullanılarak karşılaştırmalı bir başarı analizi yapılmıştır. Sonuçlar Precision, Recall and F1-Score ölçütlerine göre değerlendirilmiştir. Decision Tree algoritmasının kaza ciddiyetini sınıflandırma accuracy değeri %99.6 doğrulukla diğerleri arasında en iyi performans sağladığı belirlenmiştir.

Keywords

Sınıflandırma, kaza şiddeti, makine öğrenmesi, trafik kazası

A STUDY ON TRAFFIC CRASH SEVERITY PREDICTION USING MACHINE LEARNING ALGORITHMS

Abstract

Severity of traffic accidents holds significant importance in terms of affecting human life. Predicting the severity of traffic accidents is crucial for accident prevention and ensuring safe driving. The success of predictive methods enables the identification of relevant risk factors and the implementation of countermeasures. The causes of traffic accidents can be attributed to a wide range of variables. In particular, factors such as road disturbances, weather conditions, vehicle speed as visible causes cause traffic accidents. Obtaining real-time information about the immediate situation of an accident is challenging. However, by calculating the severity of a potential accident based on its causes, it is possible to mitigate future accidents. In this study, machine learning algorithms were tested on a dataset of traffic accidents collected in the US states between 2016 and 2023 to predict accident severity. Comparative performance analysis was conducted using machine learning techniques such as Decision Tree, Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naive Bayes (NB) to identify contributing factors to accidents. The results were evaluated according to Precision, Recall, and F1-Score metrics. It has been determined that the accuracy of the accident severity classification accuracy of the Decision Tree algorithm provides the best performance among others with an accuracy of 99.6%. This outcome signifies the potential of advanced predictive methods in significantly reducing the occurrence of traffic accidents through targeted interventions based on identified risk factors. This study's findings underscore the pivotal role of machine learning algorithms in enhancing the accuracy of traffic accident severity prediction.

Keywords

Classification, crash severity, machine learning, traffic accident

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