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Journals / Journal of Transportation and Logistics / 2026 / Volume: 9 Issue: 1

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

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

Hüseyin Korkmaz(ISTANBUL UNIVERSITY, ACADEMY OF TRANSPORTATION AND LOGISTICS, DEPARTMENT OF TRANSPORTATION AND LOGISTICS)0000-0002-2438-6919
Mehmet Ali Ertürk(İstanbul University, Faculty of Computer and Information Technologies)0000-0002-4030-1110
Mehmet Adak(ISTANBUL UNIVERSITY, ACADEMY OF TRANSPORTATION AND LOGISTICS, DEPARTMENT OF TRANSPORTATION AND LOGISTICS)0000-0002-7788-1227
Pages
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Publication Date
2024-05-17
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Traffic accident duration is defined as the time difference between the occurrence and the return of the accident scene’s initial state.
📊
Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:2013 Verileri
⚙️Variables:Accident Duration · Intelligent Transportation Systems · Machine Learning

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.

Özet

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.

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Cite This Article

Korkmaz, H., Ertürk, M. A., Adak, M. (2024). A Comparison of the Performance of Ensemble Tree and Neural Networks for The Prediction of Traffic Accident Duration. Journal of Transportation and Logistics, Volume: 9 Issue: 1. https://doi.org/10.26650/JTL.2024.1444128

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