Predicting Traffic Accident Severity Using Machine Learning Techniques

Year 2022, , 79 - 83, 29.09.2022

Ali Çelik , Onur Sevli

https://doi.org/10.46810/tdfd.1136432

Cited By: 5

Abstract

Ülkelerin ekonomilerine, milli varlıklarına zarar verip insanların yaşamlarına sebep olan trafik kazaları, ülkelerin en büyük sorunlarından biridir. Dolayısıyla, kazaların meydana gelmesine katkıda bulunan faktörlerin araştırılması ve doğru bir kaza şiddeti tahmin modelinin geliştirilmesi kritik öneme sahiptir. Bu çalışmada, 2011-2021 yılları arasında Teksas'ın Austin, Dallas ve San Antonio şehirlerinden toplanan trafik kazası verileri kullanılarak, kazalara sebep olan faktörler incelenip, Derin Öğrenme, Lojistik Regresyon, XGBoost, Random Forest, KNN ve SVM gibi 6 farklı makine öğrenme tekniğinin kaza şiddet-tahmin performans sonuçları karşılaştırılırdı. Elde edilen bulgular, Lojistik Regresyon algoritmasının kaza şiddetini sınıflandırmada %88 doğrulukla diğerleri arasında en iyi performansı gösterdiğini göstermektedir.

Keywords

Machine Learning, Deep Learning, Traffic Accident, Data Mining, Crash Severity

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