Türkiye Karayollarında Ölümlü-Yaralanmalı Trafik Kazalarına Neden Olan Sürücü Kusurlarının Analizi

Abstract

Trafikteki araç yoğunluğunun artması ve gelişen nüfus sayıları ile trafik kazalarında hayatını kaybedenlerin sayısı her geçen gün artmaktadır. Trafik güvenliğiyle ilgili birçok resmi ve özel kuruluş karayolu trafik kazalarının analizinden yararlanarak güvenlik politikaları geliştirmektedir. Bu çalışmada Türkiye genelinde trafik kazalarının bazı tanımlayıcı faktörlerinin kaza sonuçları ile ilişkisi analiz edilmektedir. Bunun için Emniyet Genel Müdürlüğü Trafik Başkanlığının yayımlamış olduğu aylık trafik istatistik bültenindeki veriler kullanılmıştır. Doğrusal regresyon analiz yöntemi kullanılarak elde edilen sonuçlar trafik kazasına neden olan sürücü kusurları ile trafik kazalarının sonuçları arasındaki ilişki düzeyini belirtmektedir.

Keywords

• Trafik kazaları
• Sürücü kusurları
• Analiz
• Basit lineer regresyon
• Korelasyon

References

1. AlKheder, S., AlRukaibi, F., Aiash, A.,2020. Risk Analysis of Traffic Accidents’ Severities: An Application of Three Data Mining Models. ISA Transactions, 106, 213-220.
2. Antariksawan, R., Mustofa, M., 2020. The Direction of Police Community Policy in the Prevention of Traffic Accidents in Polda Metro Jaya. Jurnal Cita Hukum, 8(1).
3. Casado-Sanz, N., Guirao, B., Attard, M., 2020. Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective. Sustainability, 12(6), 2237.
4. Čubranić-Dobrodolac, M., Švadlenka, L., Čičević, S., Dobrodolac, M., 2020. Modelling Driver Propensity for Traffic Accidents: A Comparison of Multiple Regression Analysis and Fuzzy Approach. International Journal of Injury Control and Safety Promotion, 27(2), 156-167.
5. Deb, R., Liew, A. W.-C., 2016. Missing Value Imputation for the Analysis of Incomplete Traffic Accident Data. Information Sciences, 339, 274-289.
6. Emniyet-Genel-Müdürlüğü-Trafik-Başkanlığı (EGMTB), 2022. Karayolu aylık trafik kaza istatistikleri. http://trafik.gov.tr/istatistikler37, Erişim tarihi: 24 Haziran 2022.
7. Ghadiri, S. M., Torkan, R., Mohd Sadullah, A. F., Speed Limit Compliance Index (SLCI): A Conceptual Method to Enhance the Efficiency of the Advisory Intelligent Speed Adaptation System. Journal of Advanced Transprtation, 2022, 2452922, 2022.
8. Iqbal, A., Rehman, Z. ur, Ali, S., Ullah, K., Ghani, U., 2020. Road Traffic Accident Analysis and Identification of Black Spot Locations on Highway. Civil Engineering Journal, 6(12), 2448-2456.

9. James, G., Witten, D., Hastie, T., Tibshirani, R., 2013. Linear Regression, An Introduction to Statistical Learning: with Applications in R. James, G. et al. (eds.) Springer Texts in Statistics, Springer, New York, NY, 59-128.
10. Jat, S., Tomar, R. S., Sharma, M. S. P.,2019. Traffic Congestion and Accident Prevention Analysis for Connectivity in Vehicular Ad-Hoc Network. 2019 5th International Conference on Signal Processing, Computing and Control (ISPCC), Solan, India.
11. Jusuf, A., Nurprasetio, I. P., Prihutama, A., 2017. Macro Data Analysis of Traffic Accidents in Indonesia. Journal of Engineering and Technological Sciences, 49(1), 132-143.
12. Karacasu, M., Ergül, B., Altin Yavuz, A., 2014. Estimating the Causes of Traffic Accidents Using Logistic Regression and Discriminant Analysis. International Journal of Injury Control and Safety Promotion, 21(4), 305-313.
13. Li, L., Shrestha, S., Hu, G., 2017. Analysis of Road Traffic Fatal Accidents Using Data Mining Techniques. 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), London, UK.
14. Mannering, F. L., Shankar, V., Bhat, C. R., 2016. Unobserved Heterogeneity and the Statistical Analysis of Highway Accident Data. Analytic Methods in Accident Research, 11, 1-16.
15. Mannering, F. L., 2018. Temporal Instability and the Analysis of Highway Accident Data. Analytic Methods in Accident Research, 17, 1-13.
16. Najada, H. A., Mahgoub, I., 2016. Big Vehicular Traffic Data Mining: Towards Accident and Congestion Prevention. 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).
17. Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., Mohammadian, A., 2020. Toward Safer Highways, Application of XGBoost and SHAP for Real-Time Accident Detection and Feature Analysis. Accident Analysis & Prevention, 136, 105405.
18. Wang, D., Liu, Q., Ma, L., Zhang, Y., Cong, H., 2019. Road Traffic Accident Severity Analysis: A Census-Based Study in China. Journal of Safety Research, 70, 135-147.
19. World Health Organization (WHO), 2015. Global status report on road safety 2015. https://apps.who.int/iris/handle/10665/189242, Erişim tarihi: 29 Eylül 2022.
20. Wu, Q., Zhang, G., Ci, Y., Wu, L., Tarefder, R. A., Alcántara, A. D., 2016. Exploratory Multinomial Logit Model–Based Driver Injury Severity Analyses for Teenage and Adult Drivers in Intersection-Related Crashes. Traffic Injury Prevention, 17(4), 413-422.
21. Yang, Z., Qi, Y., 2021. Rapid Analysis and Detection Algorithm and Prevention Countermeasures of Urban Traffic Accidents Under Artificial Intelligence. International Journal of Grid and Utility Computing, 12(4), 431-439.
22. Zhang, Y., Liu, T., Bai, Q., Shao, W., Wang, Q., 2018. New Systems-Based Method to Conduct Analysis of Road Traffic Accidents. Transportation Research Part F: Traffic Psychology and Behaviour, 54, 96-109.
23. Zhao, G., Li, J., Zhou, W.,2015. Regression Analysis of Association Between Vehicle Performance and Driver Casualty Risk in Traffic Accidents. 2015 International Conference on Transportation Information and Safety (ICTIS), Wuhan, China.