Research Article
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Year 2022, Volume: 6 Issue: 3, 530 - 539, 31.12.2022

Taner Ersöz Filiz Ersöz

https://doi.org/10.46519/ij3dptdi.1199614
Cited By: 1

Abstract

References

  • 1. IDC&Statista, “Data Created Worldwide 2010-2024”, https://financesonline.com/how-much-data-is-created-every-day/, May 8, 2022.
  • 2. KDnuggets, “Machine Learning Algorithms”, https://www.kdnuggets.com/ 2021/ 01/machine-learning-algorithms-2021. html, June 3, 2022.
  • 3. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P., “From data mining to knowledge discovery in databases”, AI Magazine, Vol 17, Issue 3, Pages 37, 1996.
  • 4. Ersöz, F., “Veri Madenciliği Teknikleri ve Uygulamaları”, Seçkin Yayınevi, Ankara, 2019.
  • 5. Patel, K., Fogarty, J., Landay, J., and Harrison, B., “Investigating statistical machine learning as a tool for software development”, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08), Association for Computing Machinery, New York, NY, USA, Pages 667–676, 2008.
  • 6. Sotirios P. C., Vassilis S, Petropoulos A., Stavroulakis, E., Vlachogiannakis, N., “Forecasting stock market crisis events using deep and statistical machine learning techniques”, Expert Systems with Applications, Vol. 112, Pages 353-371, 2018. 7. Ersöz F.&Çınar, Y., "Veri madenciliği ve makine öğrenimi yaklaşımlarının karşılaştırılması: Tekstil sektöründe bir uygulama", Avrupa Bilim ve Teknoloji Dergisi, Vol. 29, Pages 397-414, 2021.
  • 8. Ulaştırma ve Altyapı Bakanlığı, “Ulaşan ve Erişen Türkiye Raporu”. https://www. uab.gov.tr/uploads/pages/bakanlik-yayinlari/ ulasan-ve-erisen-turkiye-2021.pdf., June 11, 2022.
  • 9. Prato, C., Bekhor, S., Gal-Tzur, A., Mahalel, D., Prashker, J., “Exploring the potential of data mining techniques for the analysis of accident patterns, 12th World Conference on Transportation Research. Lisbon, Portugal, 2010.
  • 10. Elvik, R., “A re-parameterisation of the power model of the relationship between the speed of traffic and the number of accidents and accident victims”, Accident Analysis & Prevention, Vol 50, Pages 854–860, 2013.
  • 11. Martín, L., Baena, L., Garach, L., López, G. and de Oña, J., “Using data mining techniques to road safety improvement in Spanish roads”, Procedia - Social and Behavioral Sciences, Vol. 160, Pages 607 – 614, 2014.
  • 12. Gupta, M., Solanki, V. K. and Smith, V. K., “Analysis of data mining technique for traffic accident severity problem: a review”, Second International Conference on Research in Intelligent and Computing in Engineering, ACSIS, Vol. 10, Pages 197–199, 2017.
  • 13. Shakil, F.A., Hossain, S.M., Hossain, R.&Momen, S., “Prediction of road accidents using data mining techniques” In Proceedings of International Conference on Computational Intelligence and Emerging Power System, Pages 25-35. Springer, Singapore, 2022.
  • 14. Dağlı E., Büber M., Taspınar Y.S., “Detection of accident situation by machine learning methods using traffic announcements: the case of metropol Istanbul”, International Journal of Applied Mathematics Electronics and Computers, Vol. 10, Issue 3, Pages 61-67, 2022.
  • 15. Çelik A.&Sevli O., “Predicting traffic accident severity using machine learning techniques, TJNS, Vol. 11, Issue 3, Pages 79-83, 2022.
  • 16. Zhu, S., “Analyse vehicle–pedestrian crash severity at intersection with data mining techniques”, International Journal of Crashworthiness, Vol. 27, Issue 5, Pages 382, 1374, 2021.
  • 17. Sharaf A., Fahad A.& Ahmad A., “Risk analysis of traffic accidents’ severities: An application of three data mining models”, ISA Transactions, Vol. 106, Issue 2, Pages 213-220, 2020.
  • 18. Özden, C. Acı, Ç., Analysis of injury traffic accidents with machine learning methods: Adana case” Pamukkale Univ. Müh. Bilim Derg., Vol 24, Issue 2, Pages 266-275, 2018.
  • 19. Berthold, M., Cebron, N., Dill, F., Gabriel, T., Kötter, T., Meinl, T., Ohl, P., Thiel, K., Wiswedel, B., "KNIME-the Konstanz information miner", ACM SIGKDD Explorations Newsletter, Vol 11, Issue 1, Page 26, 2009.
  • 20. Ho T.K., “Random decision forests”, In: Proceedings of 3rd International Conference on Document Analysis and Recognition. IEEE, Pages 278–282, 1995.
  • 21. Analytics Vidhya, “Understanding Random Forest”,https://www.analyticsvidhya.com/ blog 2021/06/understanding-random-forest/, June 5, 2022.
  • 22. Knime Developers, https://www.knime. com/developers, September 5, 2022.
  • 23. Martinez, J.C., “The 7 Steps of Machine Learning”,https://livecodestream. dev/post /7-steps-of-machine-learning/, June 2, 2017.
  • 24. Bergstra, J., Ca, J. B., & Ca, Y. B., “Random search for hyper-parameter optimization Yoshua Bengio”, Journal of Machine Learning Research, Vol. 13, Pages 281–305, 2012.
  • 25. Mayo, M, “Frameworks for Approaching the Machine Learning Process”, https://www. kdnuggets.com/2018/05/general-approaches-machine-learning-process.html, October 19, 2022.

DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES

Year 2022, Volume: 6 Issue: 3, 530 - 539, 31.12.2022

Taner Ersöz Filiz Ersöz

https://doi.org/10.46519/ij3dptdi.1199614
Cited By: 1

Abstract

Due to the increasing number of deaths and injuries in traffic accidents today, it has become necessary to examine the potential contributing risk factors. The increase in the number of vehicles today leads to an increase in traffic accidents and loss of life and property. Analytical models are presented to investigate the socio-economic, demographic and temporal effects of the factors affecting the level of injury resulting from traffic accidents. By examining the data of various traffic accidents and developing a model, the factors and hazards affecting traffic accidents can be determined by data mining and machine learning approaches. The aim of this study is to determine which classification techniques are important for analyzing traffic accidents and to find out the factor that affects traffic accidents among the variables used in the research. The "Random Forest" algorithm, which gives the best model result among the techniques used in the research, was found. Weather conditions were found to be the most important factor among the factors that lead to traffic accidents, followed by the age and education of the driver. This study is a traceable application in terms of revealing the differences between data mining and machine learning and following the processes.

Keywords

Traffic Accident Accident Analysis Data Mining Machine Learning Classification Random Forest

References

  • 1. IDC&Statista, “Data Created Worldwide 2010-2024”, https://financesonline.com/how-much-data-is-created-every-day/, May 8, 2022.
  • 2. KDnuggets, “Machine Learning Algorithms”, https://www.kdnuggets.com/ 2021/ 01/machine-learning-algorithms-2021. html, June 3, 2022.
  • 3. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P., “From data mining to knowledge discovery in databases”, AI Magazine, Vol 17, Issue 3, Pages 37, 1996.
  • 4. Ersöz, F., “Veri Madenciliği Teknikleri ve Uygulamaları”, Seçkin Yayınevi, Ankara, 2019.
  • 5. Patel, K., Fogarty, J., Landay, J., and Harrison, B., “Investigating statistical machine learning as a tool for software development”, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '08), Association for Computing Machinery, New York, NY, USA, Pages 667–676, 2008.
  • 6. Sotirios P. C., Vassilis S, Petropoulos A., Stavroulakis, E., Vlachogiannakis, N., “Forecasting stock market crisis events using deep and statistical machine learning techniques”, Expert Systems with Applications, Vol. 112, Pages 353-371, 2018. 7. Ersöz F.&Çınar, Y., "Veri madenciliği ve makine öğrenimi yaklaşımlarının karşılaştırılması: Tekstil sektöründe bir uygulama", Avrupa Bilim ve Teknoloji Dergisi, Vol. 29, Pages 397-414, 2021.
  • 8. Ulaştırma ve Altyapı Bakanlığı, “Ulaşan ve Erişen Türkiye Raporu”. https://www. uab.gov.tr/uploads/pages/bakanlik-yayinlari/ ulasan-ve-erisen-turkiye-2021.pdf., June 11, 2022.
  • 9. Prato, C., Bekhor, S., Gal-Tzur, A., Mahalel, D., Prashker, J., “Exploring the potential of data mining techniques for the analysis of accident patterns, 12th World Conference on Transportation Research. Lisbon, Portugal, 2010.
  • 10. Elvik, R., “A re-parameterisation of the power model of the relationship between the speed of traffic and the number of accidents and accident victims”, Accident Analysis & Prevention, Vol 50, Pages 854–860, 2013.
  • 11. Martín, L., Baena, L., Garach, L., López, G. and de Oña, J., “Using data mining techniques to road safety improvement in Spanish roads”, Procedia - Social and Behavioral Sciences, Vol. 160, Pages 607 – 614, 2014.
  • 12. Gupta, M., Solanki, V. K. and Smith, V. K., “Analysis of data mining technique for traffic accident severity problem: a review”, Second International Conference on Research in Intelligent and Computing in Engineering, ACSIS, Vol. 10, Pages 197–199, 2017.
  • 13. Shakil, F.A., Hossain, S.M., Hossain, R.&Momen, S., “Prediction of road accidents using data mining techniques” In Proceedings of International Conference on Computational Intelligence and Emerging Power System, Pages 25-35. Springer, Singapore, 2022.
  • 14. Dağlı E., Büber M., Taspınar Y.S., “Detection of accident situation by machine learning methods using traffic announcements: the case of metropol Istanbul”, International Journal of Applied Mathematics Electronics and Computers, Vol. 10, Issue 3, Pages 61-67, 2022.
  • 15. Çelik A.&Sevli O., “Predicting traffic accident severity using machine learning techniques, TJNS, Vol. 11, Issue 3, Pages 79-83, 2022.
  • 16. Zhu, S., “Analyse vehicle–pedestrian crash severity at intersection with data mining techniques”, International Journal of Crashworthiness, Vol. 27, Issue 5, Pages 382, 1374, 2021.
  • 17. Sharaf A., Fahad A.& Ahmad A., “Risk analysis of traffic accidents’ severities: An application of three data mining models”, ISA Transactions, Vol. 106, Issue 2, Pages 213-220, 2020.
  • 18. Özden, C. Acı, Ç., Analysis of injury traffic accidents with machine learning methods: Adana case” Pamukkale Univ. Müh. Bilim Derg., Vol 24, Issue 2, Pages 266-275, 2018.
  • 19. Berthold, M., Cebron, N., Dill, F., Gabriel, T., Kötter, T., Meinl, T., Ohl, P., Thiel, K., Wiswedel, B., "KNIME-the Konstanz information miner", ACM SIGKDD Explorations Newsletter, Vol 11, Issue 1, Page 26, 2009.
  • 20. Ho T.K., “Random decision forests”, In: Proceedings of 3rd International Conference on Document Analysis and Recognition. IEEE, Pages 278–282, 1995.
  • 21. Analytics Vidhya, “Understanding Random Forest”,https://www.analyticsvidhya.com/ blog 2021/06/understanding-random-forest/, June 5, 2022.
  • 22. Knime Developers, https://www.knime. com/developers, September 5, 2022.
  • 23. Martinez, J.C., “The 7 Steps of Machine Learning”,https://livecodestream. dev/post /7-steps-of-machine-learning/, June 2, 2017.
  • 24. Bergstra, J., Ca, J. B., & Ca, Y. B., “Random search for hyper-parameter optimization Yoshua Bengio”, Journal of Machine Learning Research, Vol. 13, Pages 281–305, 2012.
  • 25. Mayo, M, “Frameworks for Approaching the Machine Learning Process”, https://www. kdnuggets.com/2018/05/general-approaches-machine-learning-process.html, October 19, 2022.
There are 24 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Taner Ersöz 0000-0001-5523-5120

Filiz Ersöz 0000-0002-4964-8487

Early Pub Date October 14, 2022
Publication Date December 31, 2022
Submission Date November 4, 2022
Published in Issue Year 2022 Volume: 6 Issue: 3

Cite

APA Ersöz, T., & Ersöz, F. (2022). DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES. International Journal of 3D Printing Technologies and Digital Industry, 6(3), 530-539. https://doi.org/10.46519/ij3dptdi.1199614
AMA Ersöz T, Ersöz F. DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES. IJ3DPTDI. December 2022;6(3):530-539. doi:10.46519/ij3dptdi.1199614
Chicago Ersöz, Taner, and Filiz Ersöz. “DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES”. International Journal of 3D Printing Technologies and Digital Industry 6, no. 3 (December 2022): 530-39. https://doi.org/10.46519/ij3dptdi.1199614.
EndNote Ersöz T, Ersöz F (December 1, 2022) DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES. International Journal of 3D Printing Technologies and Digital Industry 6 3 530–539.
IEEE T. Ersöz and F. Ersöz, “DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES”, IJ3DPTDI, vol. 6, no. 3, pp. 530–539, 2022, doi: 10.46519/ij3dptdi.1199614.
ISNAD Ersöz, Taner - Ersöz, Filiz. “DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES”. International Journal of 3D Printing Technologies and Digital Industry 6/3 (December 2022), 530-539. https://doi.org/10.46519/ij3dptdi.1199614.
JAMA Ersöz T, Ersöz F. DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES. IJ3DPTDI. 2022;6:530–539.
MLA Ersöz, Taner and Filiz Ersöz. “DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES”. International Journal of 3D Printing Technologies and Digital Industry, vol. 6, no. 3, 2022, pp. 530-9, doi:10.46519/ij3dptdi.1199614.
Vancouver Ersöz T, Ersöz F. DATA MINING AND MACHINE LEARNING APPROACHES IN DATA SCIENCE: PREDICTIVE MODELING OF TRAFFIC ACCIDENT CAUSES. IJ3DPTDI. 2022;6(3):530-9.

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