SavedAdd your journal

Journals / Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2026 / Cilt: 8 Sayı: 1

Evaluation of Relations Between Factors Affecting Traffic Accidents by PCA Biplot Analysis Method

Trafik Kazalarına Etki Eden Faktörler Arasındaki İlişkilerin TBA Biplot Analiz Yöntemi İle Değerlendirilmesi

Emre Tercan0000-0001-6309-1083
Erkan Beşdok0000-0001-9309-375X
Pages
103–111
Publication Date
2018-03-31
🏛️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 show that day (night), day (daylight), multi-vehicle, single vehicle, accident type (run-off-road), no intersection and intersection parameters are the most important parameters in fatal and injured accidents.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Traffic accidents are among the most important problems in Turkey.
📊
Key Findings & Data:Traffic accidents cause a lot of loss of life and property and cause negative social and psychological effects on the society.
💡
Conclusion & Impact:The results show that day (night), day (daylight), multi-vehicle, single vehicle, accident type (run-off-road), no intersection and intersection parameters are the most important parameters in fatal and injured accidents.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Trafik kazası · karayolu · biplot analizi

Abstract

Traffic accidents are among the most important problems in Turkey. Traffic accidents cause a lot of loss of life and property and cause negative social and psychological effects on the society. Determination of the conditions that could result in a fatal or injured accident is very important for taking preventive, warning and preventive measures. For this reason, it is necessary to evaluate the relationships between the parameters that cause traffic accidents. If the number of variables is more than twice, it is very complicated to visualize the multivariate data visually. The Biplot analysis method is very useful for interpreting a multivariate dataset with less geometric dimensions. In this article, the traffic accidents data set on seven different divided roadways was evaluated by Principal Components Analysis (PCA) biplot analysis method. The aim of this study is to investigate the relationships between parameters that cause traffic accidents resulting in fatal and injury. The results show that day (night), day (daylight), multi-vehicle, single vehicle, accident type (run-off-road), no intersection and intersection parameters are the most important parameters in fatal and injured accidents. The evaluation of the relationships between parameters affecting traffic accidents by biplot method will contribute to the development of preventive measures to reduce traffic accidents and will constitute an important ground for future studies.

Özet

Trafik kazaları Türkiye’de en önemli sorunlar arasında yer almaktadır. Trafik kazaları çok sayıda can ve

🔖

Cite This Article

Tercan, E., Beşdok, E. (2018). Evaluation of Relations Between Factors Affecting Traffic Accidents by PCA Biplot Analysis Method. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 8 Sayı: 1, 103–111. https://doi.org/10.21597/jist.407848

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (35)

  1. Al-Ghamdi A, 2002. Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis and Prevention, 34 (6): 729-741.
  2. Alikhani M, Nedaie A, Ahmadvand A, 2013. Presentation of clustering classification heuristic method for improvement accuracy in classification of severity of road accidents in Iran. Safety Science, 60: 142-150.
  3. Alkan BB, 2011. Çok Değişkenli İstatistiksel Yöntemlerde Biplot Tekniği. Ankara Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi, Ankara, 121 s.
  4. Alkan BB, Atakan C, Akdi Y, 2015. Visual analysis using biplot techniques of rainfall changes over Turkey. MAPAN-Journal Of Metrology Society Of India, 30 (1): 25-30.
  5. Alkan BB, Atakan C, 2011. Use of canonical variate analysis biplot in examination of choline content data of some foods. International Journal of Food Sciences and Nutrition, 62 (2):171-174.
  6. Chang LY, Chien JT, 2013. Analysis of driver injury severity in truck-involved accidents using a non parametric classification tree model. Safety Science, 51: 17-22.
  7. Çodur MY, Tortum A, Çodur M, 2013. Genelleştirilmiş lineer regresyon ile Erzurum Kuzey Çevre Yolu kaza tahmin modeli. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 3 (1): 79-84.
  8. De Oña J, Mujalli RO, Calvo FJ, Lopez G, 2013. Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis and Prevention, 51: 1-10.
  9. Eckart C, Young G, 1936. The approximation of one matrix by another of lower rank, Psychometrika 1: 211-218.
  10. Esteban VA, Villardón MPG, Sánchez IMG, 2017. Cultural values on CSR patterns and evolution: A study from the biplot representation. Ecological Indicators, 81: 18-29.
  11. Faes AAD, Costas R, Galindo MP, Bordons M, 2015. Unravelling the performance of individual scholars: Use of Canonical Biplot analysis to explore the performance of scientists by academic rank and scientific field. Journal of Informetrics, 9 (4): 722-733.
  12. Gabriel KR, 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58 (3): 453-467.
  13. Gabriel KR, 1978. Least squares approximation of matrices by additive and multiplicative models. Journal of the Royal Statistical Society, Series B, 40 (2): 186-196. Gardner S, le Roux NJ, 2005. Extensions of biplot methodology discriminant analysis. Journal of Classification, 22: 59-86.
  14. Gower JC, Hand DJ, 1996. Biplots. Chapman and Hall, London, 277 pp.
  15. Gower JC, 2004. The geometry of biplot scaling. Biometrika, 91 (3): 705-714.
  16. Gower JC, Lubbe S, le Roux NJ, 2011. Understanding Biplots. John Wiley and Sons, Chichester, United Kingdom.
  17. Kabak D, Akçura M, 2017. Bingöl ilinden toplanan yerel çavdarlarda tane verimi ve bazı özellikler arasındaki ilişkilerin biplot analizi ile incelenmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 4 (2): 227-235.
  18. Kadariya M, Glover KD, Mergoum M, Osborne LE, 2008. Biplot analysis of agronomic and fusarium head blight resistance traits in spring wheat. Journal of Crop Improvement, 22 (2): 147-170.
  19. Kashani AT, Mohaymany AS, 2011. Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Safety Science, 49: 1314-1320.
  20. Kendal E, Tekdal S, Aktaş H, Karaman M, Berekatoğlu K, Doğan H, 2014. Biplot analizi kullanılarak yazlık arpa genotiplerinin verim ve verim unsurlarının belirlenmesi. Trakya University Journal of Natural Sciences, 15 (2): 95-103.
  21. KGM, 2016. Trafik Kazaları Özeti 2016. Karayolları Genel Müdürlüğü, Ankara, Türkiye, 17s.
  22. Kim SK, McKay D, Taylor S, Tolin D, Olatunji B, Timpano K, Abramowitz J, 2016. The structure of obsessive compulsive symptoms and beliefs: A correspondence and biplot analysis. Journal of Anxiety Disorders, 38: 79-87.
  23. Kunt M, Aghayan I, Noii N, 2012. Prediction for traffic accident severity comparing the ANN, genetic algorithm, combined genetic algorithm and pattern search methods. Transport, 26 (4): 353-366.
  24. Librero NAB, Sierra C, Galindo VMP, Barzola RO, Villardón GMP, 2017. Clustering Disjoint HJ-Biplot: A new tool for identifying pollution patterns in geochemical studies. Chemosphere, 176: 389-396.
  25. Martín MIG, Palacios VV, Revilla I, Quintana AMV, Hierro JMH, 2017. Discrimination between cheeses made from cow’s, ewe’s and goat’s milk from unsaturated fatty acids and use of the canonical biplot method. Journal of Food Composition and Analysis, 56: 34-40.
  26. Mujalli RO, De Ona J, 2011. A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian Networks. Journal of Safety Research, 42 (5): 317-326.
  27. Oyedele OF, Lubbe S, 2015. The construction of a partial least-squares biplot, Journal of Applied Statistics, 42 (11): 2449-2460.
  28. Sayar MS, Han Y, 2015. Mürdümük (Lathyrus sativus L.) Hatlarının tohum verimi ve verim komponentlerinin belirlenmesi ve GGE biplot analiz yöntemiyle değerlendirilmesi. Tarım Bilimleri Dergisi, 21: 78-92.
  29. Sierra C, Ruíz-Barzola O, Menéndez M, Demey JR, Vicente-Villardón JL, 2017. Geochemical interactions study in surface river sediments at an artisanal mining area by means of Canonical (MANOVA)-Biplot. Journal of Geochemical Exploration, 175: 72-81.
  30. Tekdal S, Kendal E, Ayana B, 2014. İleri kademe makarnalık buğday hatlarının verim ve bazı kalite özelliklerinin biplot analiz yöntemi ile değerlendirilmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 1 (3): 322-330.
  31. Tercan E, 2017. Karayolu Güvenliğinde Esnek Hesaplama Tekniklerinin Kullanılması. Erciyes Üniversitesi Fen Bilimleri Enstitüsü, Doktora Tezi, Kayseri, 113s.
  32. Tortum A, Çodur MY, Kılınç B, 2012. Modeling traffic accidents in Turkey using regression analysis. Iğdır University Journal of the Institute of Science and Technology, 2 (3): 69-78.
  33. WHO, 2016. World health statistics 2016: Monitoring Health for the SDGs. ISBN 978 92 4 156526 4, Switzerland.
  34. Yau K, 2004. Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong. Accident Analysis and Prevention, 36 (3): 333-340.
  35. Zhang PP, Song H, Ke XW, Jin XJ, Yin LH, Liu Y, Qu Y, Su W, Feng NJ, Zheng DF, Feng BL, 2016. GGE biplot analysis of yield stability and test location representativeness in proso millet (Panicum miliaceum L.) genotypes. Journal of Integrative Agriculture, 15 (6): 1218-1227.