SavedAdd your journal

Journals / Istanbul University Journal of Electrical and Electronics Engineering / 2017 / Cilt: 17 - Sayı: 2

THE MODELLING RECORDED FAULTS IN RAILWAYS AND PREDICTION

Emrah BAL
Hasan TIRYAKI
Ilhan KOCAARSLAN
Suleyman KARAMAN
Pages
3417–3423
Publication Date
2022-10-18
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

The results, which were acquired by this article, are considered as a guide because of the absence of any studies about the prediction of the accidents caused by the faults.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper describes the prediction of the accidents and incidents by modelling the faults that are hard to be predicted, and forms the stochastic processes in railways.
📊
Key Findings & Data:As it is seen, the root causes of the occurrence of the accidents and incidents in railways are the technical faults, the organizational faults, the staff faults, the third-party faults, the other faults and some parameters affecting these faults holidays, seasonal effects etc.
💡
Conclusion & Impact:The results, which were acquired by this article, are considered as a guide because of the absence of any studies about the prediction of the accidents caused by the faults.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This paper describes the prediction of the accidents and incidents by modelling the faults that are hard to be predicted, and forms the stochastic processes in railways. As it is seen, the root causes of the occurrence of the accidents and incidents in railways are the technical faults, the organizational faults, the staff faults, the third-party faults, the other faults and some parameters affecting these faults holidays, seasonal effects etc. The system model was set up by considering the above-mentioned faults and the causes as the inputs of the model and the accidents and the incidents as the outputs of the model. The real values of the accidents and the incidents were compared with the predicted values by using Artificial Neural Networks-ANN and Support Vector Machines-SVM. The results, which were acquired by this article, are considered as a guide because of the absence of any studies about the prediction of the accidents caused by the faults.

🔖

Cite This Article

BAL, E., TIRYAKI, H., KOCAARSLAN, I., KARAMAN, S. (2022). THE MODELLING RECORDED FAULTS IN RAILWAYS AND PREDICTION. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 17 - Sayı: 2, 3417–3423. https://ajindex.com/articles/543593

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Markovic N., Milinkovic S., Tikhonov K. S., Schonfeld P., "Analyzing passenger train arrival delays with support vector regression", Transportation Research Part C, vol. 56, 2015, 251-262
  2. Islam D. M. Z., Laparidou K., Burgess A., "Cost effective future derailment mitigation techniques for rail freight traffic management in Europe", Transportation Research Part C, Accepted 19 June 2015
  3. Kocaarslan İ., Karaman S., Apaydın İ., Gör E., "Demiryolu Emniyet Yönetim Sistemi ve TCDD'de Uygulanması", I. Uluslararası Raylı Sistemler Çalıştayı, Karabük /TURKEY, 2012. (In Turkish
  4. Reason J.T., "Human Error: Models And Management", British Medical Journal, vol. 320, 2000, 768-770
  5. Karabacak S. A., "Implementation of Monitoring and Analysis Method on Railway Safety Management System", Master Thesis, Istanbul University, 2014. (In Turkish
  6. Reason J.T., " Human Error", Cambridge University Press, New York, 1990, ISBN: 978-0521314190
  7. Karaman S., Kocaarslan İ., Tiryaki H., "Application of Artificial Neural Networks for Safety Management Systems in Railways", International Congress on Advanced Railway Engineering, 2-4 March 2015, İstanbul, 262-267
  8. Sivri N., Özcan K., Uçan O. N., Akıncılar O., "Estimation of Stream Temperature in Değirmendere River (Trabzon-Turkey) Using Artificial Neural Network Model", Turkish Journal of Fisheries and Aquatic Sciences, vol. 9, 2009, 145-150
  9. Alp M., Cığızoğlu K., "Rainfall-Runoff Relation to Different Methods of Artificial Neural Network Modeling.", İTU Journal/ d engineering, vol. 3(1), 2004, 80-88. (In Turkish
  10. Akgundogdu A., "Trabecular Bones Structure Analyzing Using Artificial Intelligence Methods", Ph.D. Thesis, Istanbul University, 2009. (In Turkish
  11. Cannas B., Montisci A., Fanni A., See A., Sechi G. M., "Comparing Artifıcial Neural Networks and Support Vector Machines for Modelling Rainfall-Runoff", 6th International Conference on Hydroinformatics - Liong, Phoon &Babovic (eds) © 2004 World Scientific Publishing Company, 2004, ISBN 981-238-787-0
  12. Dawson C.W., Wilby R., "An Artificial Neural Network Approach to Rainfall-runoff Modelling. Hydrological", Sciences--Journal--des Sciences Hydrologiques, 43(1) 47, February 1998, ISSN 1019-101145
  13. Kayaalp N., Hamidi N., "Dicle Nehrinde Taşınan Aylık Süspanse-Sediment Miktarının Yapay Sinir Ağları ile Belirlenmesi", IV. Ulusal Hidroloji Kongresi ve Hidrolojide Yeni Yöntemler Semineri, 21-25 June 2004. (In Turkish
  14. Sivri N., K