Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması

Hüseyin Korkmaz; Mehmet Ali Ertürk; Mehmet Adak

doi:10.26650/JTL.2024.1444128

Research Article

A Comparison of the Performance of Ensemble Tree and Neural Networks for The Prediction of Traffic Accident Duration

Year 2024, Volume: 9 Issue: 1, 20 - 47, 17.05.2024

Hüseyin Korkmaz Mehmet Ali Ertürk Mehmet Adak

https://doi.org/10.26650/JTL.2024.1444128

Abstract

Traffic accident duration is defined as the time difference between the occurrence and the return of the accident scene’s initial state. The aim of this paper is to predict the traffic accident duration based on traffic accident data in Istanbul with Ensemble Tree and Neural Networks methods and to compare the performance of these methods. The secondary aim of the paper is to identify the main factors affecting the accident duration. The accident data sets obtained from Istanbul Metropolitan Municipality and General Directorate of Security are used in this paper. The dataset includes 1.905 traffic accident records in Istanbul from 2013 to 2021. The data were analyzed within the scope of data mining. Statistical tests and machine learning algorithms were applied to the extracted data set and prediction of traffic accident duration was performed. R², MSE, RMSE and MAE metrics were used for the performance measures of the algorithms applied in this paper. It was found that the Ensemble Tree algorithm achieved an R-Square of 0.85 in training, while the Neural Networks algorithm performed better with 0.91 in testing.

Keywords

Accident Duration, Intelligent Transportation Systems, Machine Learning, Neural Networks, Ensemble Tree

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Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması

Year 2024, Volume: 9 Issue: 1, 20 - 47, 17.05.2024

Hüseyin Korkmaz Mehmet Ali Ertürk Mehmet Adak

https://doi.org/10.26650/JTL.2024.1444128

Abstract

Trafik kaza süresi, bir kazanın meydana gelmesi ile kaza yerinin başlangıç durumuna dönmesi arasındaki zaman farkı olarak ifade edilmektedir. Bu araştırmanın birincil amacı İstanbul’daki trafik kaza verilerine dayalı olarak trafik kaza süresini Topluluk Ağacı ve Sinir Ağları yöntemleri ile tahmin etmek ve bu yöntemlerin performanslarını karşılaştırmaktır. Araştırmanın ikincil amacı ise trafik kaza süresini etkileyen temel faktörleri belirlemektir. Bu araştırmada İstanbul Büyükşehir Belediyesi ve Emniyet Genel Müdürlüğü kurumlarından elde edilen İstanbul’a ait kaza bilgisi veri setleri kullanılmıştır. Veri seti, 2013-2021 yılları arasındaki İstanbul’da gerçekleşen 1.905 trafik kaza kaydını içermektedir. Veriler, veri madenciliği kapsamında incelenmiştir. Ayıklanan veri setine istatistik testleri ve makine öğrenmesi algoritmalarından Topluluk Ağacı ve Sinir Ağları uygulanarak trafik kaza süresi tahmini gerçekleştirilmiştir. Bu araştırmada uygulanan algoritmaların performans ölçümleri için R², MSE, RMSE ve MAE metrikleri kullanılmıştır. Topluluk Ağacı algoritmasının eğitimde R-Kare: 0.85 ile başarılı bir performans elde ettiği, testte ise R-Kare: 0.91 ile Sinir Ağları algoritmasının daha iyi performans gösterdiği sonucuna ulaşılmıştır.

Keywords

Kaza Süresi, Akıllı Ulaşım Sistemleri, Makine Öğrenmesi, Sinir Ağları, Topluluk Ağacı

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Primary Language	Turkish
Subjects	Transportation, Logistics and Supply Chains (Other)
Journal Section	Research Article
Authors	Hüseyin Korkmaz 0000-0002-2438-6919 Mehmet Ali Ertürk 0000-0002-4030-1110 Mehmet Adak 0000-0002-7788-1227
Early Pub Date	July 5, 2024
Publication Date	May 17, 2024
Submission Date	February 27, 2024
Acceptance Date	April 9, 2024
Published in Issue	Year 2024 Volume: 9 Issue: 1

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APA	Korkmaz, H., Ertürk, M. A., & Adak, M. (2024). Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması. Journal of Transportation and Logistics, 9(1), 20-47. https://doi.org/10.26650/JTL.2024.1444128
AMA	Korkmaz H, Ertürk MA, Adak M. Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması. JTL. May 2024;9(1):20-47. doi:10.26650/JTL.2024.1444128
Chicago	Korkmaz, Hüseyin, Mehmet Ali Ertürk, and Mehmet Adak. “Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı Ve Sinir Ağları Performansının Karşılaştırılması”. Journal of Transportation and Logistics 9, no. 1 (May 2024): 20-47. https://doi.org/10.26650/JTL.2024.1444128.
EndNote	Korkmaz H, Ertürk MA, Adak M (May 1, 2024) Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması. Journal of Transportation and Logistics 9 1 20–47.
IEEE	H. Korkmaz, M. A. Ertürk, and M. Adak, “Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması”, JTL, vol. 9, no. 1, pp. 20–47, 2024, doi: 10.26650/JTL.2024.1444128.
ISNAD	Korkmaz, Hüseyin et al. “Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı Ve Sinir Ağları Performansının Karşılaştırılması”. Journal of Transportation and Logistics 9/1 (May 2024), 20-47. https://doi.org/10.26650/JTL.2024.1444128.
JAMA	Korkmaz H, Ertürk MA, Adak M. Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması. JTL. 2024;9:20–47.
MLA	Korkmaz, Hüseyin et al. “Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı Ve Sinir Ağları Performansının Karşılaştırılması”. Journal of Transportation and Logistics, vol. 9, no. 1, 2024, pp. 20-47, doi:10.26650/JTL.2024.1444128.
Vancouver	Korkmaz H, Ertürk MA, Adak M. Trafik Kaza Süresinin Tahmini İçin Topluluk Ağacı ve Sinir Ağları Performansının Karşılaştırılması. JTL. 2024;9(1):20-47.

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The JTL is being published twice (in April and October of) a year, as an official international peer-reviewed journal of the School of Transportation and Logistics at Istanbul University.