TÜRKİYE'DEKİ TRAFİK KAZALARININ PERİYODİK YAPISININ ARAŞTIRILMASI

Abstract

Bu çalışmada, Türkiye'de 2019 yılında meydana gelen günlük trafik kazaları verilerine zaman serisi analizi uygulanmıştır. Çalışmada kullanılan verilerin en önemli özelliği kolluk birimleri tarafından günlük olarak tutulan resmi trafik kazası kayıtları olmasıdır. Bu verilerle ilgili olarak en uygun zaman serisi modeli belirlenmiş ve trafik kazalarında periyodik bileşenlerin olup olmadığı incelenmiştir. Verilerin birinci dereceden entegre olduğu görülmektedir. Bu durumda serinin birinci dereceden farkı istatistiksel sonuç açısından alınmıştır. Serinin grafikleri incelendiğinde, serilerde olası periyodikliğin bulunabileceği varsayımı ile Akdi ve Dickey (1998) tarafından önerilen periodogram temelli birim kök testi ile serinin durağanlığı da test edilmiş ve serinin % 10 anlamlılık düzeyinde durağan olduğu görülmüştür. Elde edilen sonuçlara göre 2019 yılında günlük trafik kaza sayılarında 33, 36.5 ve 73 günlük dönemlerin önemli olduğu tespit edilmiştir. 73 günlük sürenin Ramazan Bayramı ile Kurban Bayramı arasındaki döneme denk geldiği (iki dini bayram arasında 70 günlük bir ara vardır) gösterilmiştir.

Keywords

• Zaman Serisi Analizi
• Günlük Trafik Kazaları
• Dönemsellik

INVESTIGATING THE PERIODIC STRUCTURE OF TRAFFIC ACCIDENTS IN TURKEY

Abstract

In this study, a time-series analysis is applied to the daily accidents between the periods January 1, 2019 and December 31, 2019 of Turkey. The most important feature of the data used in the study is the official daily traffic accident records kept by the law enforcement units. Regarding these data, the most appropriate time series model is determined and it is examined whether there are periodic components in traffic accidents or not. It is observed that the data is first-order integrated. In this case, the difference of the series from the first order should be taken in terms of statistical conclusion. When the graphs of the series are examined, the stationarity of the series has been also tested with the periodogram-based unit root test proposed by Akdi and Dickey (1998) with the assumption that possible periodicity can be found in the series, and it has been observed that the series is stationary at the 10% significance level. According to the results 33, 36.5 and 73-day periods are significant in the number of daily traffic accidents in 2019. It has been demonstrated that the 73-day period corresponds to the period between Ramadan Feast and Feast of Sacrifice (there is a 70-day interval between the two religious holidays).

Keywords

• Time Series Analysis
• Daily Traffic Accidents
• Periodicity
• Turkey

References

1. Abdel-Aty, M. A., & Radwan, A. E. (2000). Modeling traffic accident occurrence and involvement. Accident Analysis & Prevention, 32(5), 633-642. https://doi.org/10.1016/S0001-4575(99)00094-9
2. Akdi, Y. (2012). Zaman Serileri Analizi-Birim Kökler ve Kointegrasyon, 3’nci Baskı, Gazi Kitapevi, Ankara.
3. Akdi, Y. and D. A. Dickey. (1998). Periodograms of unit root time series: distributions and test, Communications in Statistics-Theory and Methods, 27(1), 69-87. https://doi.org/10.1080/03610929808832651
4. Akdi, Y., Okkaoğlu, Y., Gölveren, E., and Yücel, M. E. (2020a) Estimation and forecasting of PM 10 air pollution in Ankara via time series and harmonic regressions. International Journal of Environmental Science and Technology, 1-14. https://doi.org/10.1007/s13762-020-02705-0
5. Akdi, Y., Varlik, S., and Berument, M. H. (2020b) Duration of Global Financial Cycles. Physica A: Statistical Mechanics and its Applications, 124331. https://doi.org/10.1016/j.physa.2020.124331
6. Akdi, Y., Gölveren, E., and Okkaoğlu, Y. (2020c) Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting. Energy, 191, 116524. https://doi.org/10.1016/j.energy.2019.116524
7. Akdi, Y. and Ünlü, K.D. (2021). Periodicity in precipitation and temperature for monthly data of Turkey. Theor Appl Climatol 143, 957–968. https://doi.org/10.1007/s00704-020-03459-y
8. Al-Harbi, M., Yassin, M. F., and Shams, M. B. (2012). Stochastic modeling of the impact of meteorological conditions on road traffic accidents. Stochastic Environmental Research and Risk Assessment, 26(5), 739-750.

9. Alkan, G., Farrow, R., Liu, H., Moore, C., Ng, H. K. T., Stokes, L., and Zhong, Y. (2021). Predictive modeling of maximum injury severity and potential economic cost in a car accident based on the General Estimates System data. Computational Statistics, 1-15.
10. Al-Turaiki, I., Aloumi, M., Aloumi, N., & Alghamdi, K. (2016, November). Modeling traffic accidents in Saudi Arabia using classification techniques. In 2016 4th Saudi International Conference on Information Technology (Big Data Analysis) (KACSTIT) (pp. 1-5). IEEE.
11. Ali, G. A., and Tayfour, A. (2012). Characteristics and prediction of traffic accident casualties in Sudan using statistical modeling and artificial neural networks. International journal of transportation science and technology, 1(4), 305-317.
12. Bayata, H. F., Hattatoğlu, F., & Karslı, N. (2011). Modeling of monthly traffic accidents with the artificial neural network method. International Journal of the Physical Sciences Vol. 6(2), 244-254.
13. Brockwell, P. J., Davis, R. A., & Fienberg, S. E. (1991). Time series: theory and methods: theory and methods. Springer Science & Business Media.
14. Chin, H. C., & Quddus, M. A. (2003). Modeling count data with excess zeroes: An empirical application to traffic accidents. Sociological methods & research, 32(1), 90-116. https://doi.org/10.1177/0049124103253459
15. Chubukov, A., Kapitanov, V., Monina, O., Silyanov, V., & Brannolte, U. (2017). Simulation of regional mortality rate in road accidents. Transportation Research Procedia, 20, 112-124. https://doi.org/10.1016/j.trpro.2017.01.031
16. Chung, Y. S., Park, R. K., & Kim, J. M. (2014). Study on predictive modeling of incidence of traffic accidents caused by weather conditions. Journal of the Korea Convergence Society, 5(1), 9-15. https://doi.org/10.15207/JKCS.2014.5.1.009
17. Dickey, D. A., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of The American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
18. Fuller, W. A. (1996). Introduction to statistical time series. 2nd ed. New York: John Wiley & Sons.
19. Gao, Z., Pan, R., Yu, R., & Wang, X. (2018). Research on automated modeling algorithm using association rules for traffic accidents. In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 127-132). IEEE. https://doi.org10.1109/BigComp.2018.00027
20. Kenneth, G. E. (2021). Statistical Application of Regression techniques in Modeling Road Accidents in Edo State, Nigeria. Sch J. Phys. Math Stat, 1, 14-18.
21. Okkaoğlu, Y., Akdi, Y., and Ünlü, K. D. (2020). Daily PM10, periodicity and harmonic regression model: The case of London. Atmospheric Environment, 117755. https://doi.org/10.1016/j.atmosenv.2020.117755
22. Singh, G., Pal, M., Yadav, Y., & Singla, T. (2020). Deep neural network-based predictive modeling of road accidents. Neural Computing and Applications, 1-10. https://doi.org/10.1007/s00521-019-04695-8
23. Taamneh, M., Alkheder, S., and Taamneh, S. (2017). Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety & Security, 9(2), 146-166.
24. Theofilatos, A., Yannis, G., Kopelias, P., & Papadimitriou, F. (2016). Predicting road accidents: a rare-events modeling approach. Transportation research procedia, 14, 3399-3405. https://doi.org/10.1016/j.trpro.2016.05.293
25. Wei, W.W.S. (2006). Time Series Analysis: Univariate and Multivariate Methods, Second Edition, Pearson Addison Wesley, Boston