TY  - JOUR
T1  - Analysis Of Traffic Accidents Using Machine Learning Under Pandemic Conditions
TT  - Makine Öğrenmesi Algoritmalarını Kullanılarak Pandemi Şartları Altında Trafik Kazalarının Analizi
AU  - Haşıloğlu Aras, Ülviye Gülsüm
PY  - 2025
DA  - April
Y2  - 2025
DO  - 10.38002/tuad.1572607
JF  - Trafik ve Ulaşım Araştırmaları Dergisi
JO  - TUAD
PB  - Bahar ÖZ
WT  - DergiPark
SN  - 2667-8071
SP  - 47
EP  - 57
VL  - 8
IS  - 1
LA  - en
AB  - The COVID-19 pandemic that emerged in 2019 affected all aspects of life, including spiritual, psychological, social, economic, health and transportation aspects. Despite its negative consequences, however, the COVID-19 pandemic also produced some positive results. This study investigated the effect of COVID-19 lockdowns on killed-and-injured traffic accidents in metropolitan cities and Zonguldak Province in Turkey from 2012–2019 using the Extreme Gradient Boost (XGBoost) algorithm. Nonlinear regression analyses were performed using machine learning in Python programming language on the Google Colab platform. The analysis provided an estimated number of accidents for 2020, which was compared with the real killed-and-injured accidents data from metropolitan cities and in Zonguldak in 2020. The comparison showed that COVID-19 lockdowns caused a decrease in traffic accidents in metropolitan cities and Zonguldak Province, except in Diyarbakır and Ordu. It has been revealed that the number of traffic accidents predicted by machine learning algorithms in metropolitan areas for 2020 is 18.3% higher than the number of traffic accidents in 2020. Therefore, although accurate predictions can be made with machine learning, it has been observed that there may be a margin of error in extraordinary situations such as earthquakes, wars and pandemics.
KW  - Lockdown
KW  - Traffic accidents
KW  - Accident prediction model
KW  - Machine learning
KW  - Extreme gradient boost
N2  - 2019 yılında ortaya çıkan COVID-19 pandemisi, ruhsal, psikolojik, sosyal, ekonomik, sağlık ve ulaşım olmak üzere hayatın tüm alanlarına etki ettiği gözlemlenmiştir. COVID-19 ‘dan kaynaklı pandemi süreci birçok olumsuz sonuçlarına rağmen, bazı olumlu sonuçlar da doğurmuştur.  Bu çalışmada, pandemi sürecinin olumlu sonuçlarından biri detaylı olarak ele alınmıştır. Çalışma, Türkiye’de Zonguldak ili de dahil olmak üzere tüm büyükşehirleri kapsamaktadır. Bu çalışma ile, COVID-19 karantinalarının 2012-2019 yılları arasında gerçekleşen ölümlü ve yaralanmalı trafik kazaları üzerindeki etkisi, Extreme Gradient Boost (XGBoost) algoritması kullanılarak detaylı olarak araştırılmıştır. Google Colab platformunda Python programlama dilinde makine öğrenmesi kullanılarak doğrusal olmayan regresyon analizleri yapılmıştır. Analiz sonucunda 2020 yılı için tahmini kaza sayısı elde edilmiş ve bu sayı 2020 yılında büyükşehirlerde ve Zonguldak'ta meydana gelen gerçek ölümlü ve yaralanmalı kaza verileri ile karşılaştırılmıştır. Bu analiz, COVID-19 karantinalarının Zonguldak, Diyarbakır ve Ordu illeri hariç tüm büyükşehirlerde trafik kazalarında azalmaya neden olduğunu göstermiştir. Makine öğrenimi algoritmaları ile 2020 yılı için büyükşehirlerde tahmin edilen trafik kaza sayılarının, 2020 yılında gerçekleşen trafik kaza sayılarına göre %18,3 oranında daha yüksek olduğu ortaya çıkmıştır. Dolayısıyla Makine öğrenmesi ile doğru tahminler yapılabilse de deprem, savaş ve pandemi gibi olağanüstü durumlarda hata payı olabileceği gözlemlenmiştir.
CR  - Aloi, A., Alonso, B., Benavente, J., Cordera, R., Echániz, E., González, F., Ladisa, C., Lezama-Romanelli, R., López-Parra, Á., Mazzei, V., Perrucci, L., Prieto-Quintana, D., Rodríguez, A., & Sañudo, R. (2020). Effects of the COVID-19 Lockdown on Urban Mobility: Empirical Evidence from the City of Santander (Spain). Sustainability, 12(9), 3870. https://doi.org/10.3390/su12093870
CR  - Bayraktutan, Y., & Özbilgin, M. (2013). Türkiye’de İller Düzeyinde Karayolu Yük Trafiği Dağılımının Analizi. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 17(2), 81-92.
CR  - Bazlamit, S. M., Eid, A., & Al-mofleh, H. (2020). Impact of fasting on traffic accidents. Jordan Journal of Civil Engineering, 14(2), 431–442.
CR  - Brodeur, A., Cook, N., & Wright, T. (2021). On the effects of COVID-19 safer-at-home policies on social distancing, car crashes and pollution. Journal of Environmental Economics and Management, 106, 102427. https://doi.org/10.1016/j.jeem.2021.102427
CR  - Chang, L.-Y., & Wang, H.-W. (2006). Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis & Prevention, 38(5), 1019–1027. https://doi.org/10.1016/j.aap.2006.04.009
CR  - Chong, M., Abraham, A., & Paprzycki, M. (2005). No TitleTraffic accident analysis using machine learning paradigms. Informatica, 29(1), 89–98.
CR  - Christey, G., Amey, J., Campbell, A., & Smith, A. (2020). Variation in volumes and characteristics of trauma patients admitted to a level one trauma centre during national level 4 lockdown for COVID-19 in New Zealand. The New Zealand Medical Journal, 133(1513), 81-88.
CR  - Deok, K. C. (2019). Data-Driven Materials Modeling with XGBoost Algorithm and Statistical Inference Analysis for Prediction of Fatigue Strength of Steels. International Journal of Precision Engineering and Manufacturing, 20(1), 129–138. https://doi.org/10.1007/s12541-019-00048-6
CR  - Envepo A.Ş. (2018) Makine Öğrenmesi Nasıl Çalışır? Makine Öğrenme Algoritmaları. https://www.envepo.com/blog/makine-ogrenmesi-nasil-calisir-makine-ogrenme-algoritmalari
CR  - Emniyet Genel Müdürlüğü Trafik Başkanlığı (2020). Trafik İstatistik Bülteni Ülke Geneli̇. https://trafik.gov.tr/kurumlar/trafik.gov.tr/04-Istatistik/Aylik/202503/Mart-2025.pdf
CR  - Gökdaǧ, M., Kaya, M. D., Atalay, A., & Haşiloǧlu, A. S. (2004). Injuries and fatalities in Turkish road traffic accidents. Proceedings of the Institution of Civil Engineers: Transport, 157(4), 231–237. https://doi.org/10.1680/tran.2004.157.4.231
CR  - Ibrahem Ahmed Osman, A., Najah Ahmed, A., Chow, M. F., Feng Huang, Y., & El-Shafie, A. (2021). Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12(2),1545-1556. https://doi.org/10.1016/j.asej.2020.11.011
CR  - Jayavel, M., & Lizy, M. L. (2014). Knowledge and Attitude Regarding prevention of Road Traffic Accidents among Adolescents. Innovations in Pharmaceuticals and Pharmacotherapy (IPP), 2(1), 328–339.
CR  - Jovic Vranes, A., Bjegovic Mikanovic, V., Milin Lazovic, J., & Kosanovic, V. (2018). Road traffic safety as a public health problem: Evidence from Serbia. Journal of Transport and Health, 8, 55–62. https://doi.org/10.1016/j.jth.2017.12.005
CR  - Korkmaz, E. (2022). COVID-19 Salgın Sürecinin Toplu Taşıma Sistemlerine Etkisinin Anket Yöntemiyle İstanbul-Ankara İçin İncelenmesi. Afet ve Risk Dergisi, 5(1), 247-260. https://doi.org/10.35341/afet.1060291
CR  - Kwon, O. H., Rhee, W., & Yoon, Y. (2015). Application of classification algorithms for analysis of road safety risk factor dependencies. Accident Analysis & Prevention, 75, 1–15. https://doi.org/10.1016/j.aap.2014.11.005
CR  - Lin, L., Shi, F., & Li, W. (2021). Assessing inequality, irregularity, and severity regarding road traffic safety during COVID-19. Scientific Reports, 11(1), 1-7. https://doi.org/10.1038/s41598-021-91392-z
CR  - Lin, Y., & Li, R. (2020). Real-time traffic accidents post-impact prediction: Based on crowdsourcing data. Accident Analysis and Prevention, 145(2020), 1-11 105696. https://doi.org/10.1016/j.aap.2020.105696
CR  - Liu, W. Q., Hwang, B. G., Shan, M., & Looi, K. Y. (2019). Prefabricated Prefinished Volumetric Construction: Key Constraints and Mitigation Strategies. IOP Conference Series: Earth and Environmental Science, 385(1), 1-7. https://doi.org/10.1088/1755-1315/385/1/012001
CR  - Ma, J., Ding, Y., Cheng, J. C. P., Tan, Y., Gan, V. J. L., & Zhang, J. (2019). Analyzing the Leading Causes of Traffic Fatalities Using XGBoost and Grid-Based Analysis: A City Management Perspective. IEEE Access, 7, 148059–148072. https://doi.org/10.1109/ACCESS.2019.2946401
CR  - Metintas, S. (2020). Epidemiology of COVID-19. Eurasian Journal of Pulmonology, 22(4), 2-7. https://doi.org/10.4103/ejop.ejop_55_20
CR  - Muhammad, L. J., Salisu, S., Yakubu, A., Malgwi, Y. M., Abdullahi, E. T., Mohammed, I. . A., & Muhammad, N. A. (2017). Using Decision Tree Data Mining Algorithm to Predict Causes of Road Traffic Accidents, its Prone Locations and Time along Kano –Wudil Highway. International Journal of Database Theory and Application, 10(1), 197–206. https://doi.org/10.14257/ijdta.2017.10.1.18
CR  - Nuñez, J. H., Sallent, A., Lakhani, K., Guerra-Farfan, E., Vidal, N., Ekhtiari, S., & Minguell, J. (2020). Impact of the COVID-19 Pandemic on an Emergency Traumatology Service: Experience at a Tertiary Trauma Centre in Spain. Injury, 51(7), 1414–1418. https://doi.org/10.1016/j.injury.2020.05.016
CR  - Oguzoglu, U. (2020). Covid-19 Lockdowns and Decline in Traffic Related Deaths and Injuries. SSRN Electronic Journal. 13278, 19, 1-17.  https://doi.org/10.2139/ssrn.3608527
CR  - Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. (Kouros). (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis and Prevention, 136(2020), 105405. https://doi.org/10.1016/j.aap.2019.105405
CR  - Qureshi, A. I., Huang, W., Khan, S., Lobanova, I., Siddiq, F., Gomez, C. R., & Suri, M. F. K. (2020). Mandated societal lockdown and road traffic accidents. Accident Analysis and Prevention, 146(2020), 105747. https://doi.org/10.1016/j.aap.2020.105747
CR  - Rezapour Mashhadi, M. M., Saha, P., & Ksaibati, K. (2017). Impact of traffic Enforcement on Traffic Safety. International Journal of Police Science & Management, 19(4), 238–246. https://doi.org/10.1177/1461355717730836
CR  - Saladié, Ò., Bustamante, E., & Gutiérrez, A. (2020). COVID-19 lockdown and reduction of traffic accidents in Tarragona province, Spain. Transportation Research Interdisciplinary Perspectives, 8(2020), 100218. https://doi.org/10.1016/j.trip.2020.100218
CR  - Selimoğlu, Eda. (2014). Trafik Kazalarının Nedenleri , Sonuçları ve Kazaların Önlenmesine İlişkin Öneriler. Ziraat Mühendisliği Dergisi, 361, 51–54.
CR  - Shilling, F., & Waetjen, D. (2020). Special Reports: Impact of COVID-19 on California Traffic Crashes. Road Ecology Center https://roadecology.ucdavis.edu/content/reports-covid-19-mitigation-and-traffic
CR  - Sohn, S. Y., & Shin, H. (2001). Pattern recognition for road traffic accident severity in Korea. Ergonomics, 44(1), 107–117. https://doi.org/10.1080/00140130120928
CR  - Tang, J., Zheng, L., Han, C., Liu, F., & Cai, J. (2020). Traffic Incident Clearance Time Prediction and Influencing Factor Analysis Using Extreme Gradient Boosting Model. Journal of Advanced Transportation, 2020, 1-12. https://doi.org/10.1155/2020/6401082
CR  - Türkiye İstatistik Kurumu. (2020). Türkiye İstatistik Kurumu. Türkiye karayollarında 2012-2018 yılları arasında gerçekleşen trafik kazaları verileri. https://data.tuik.gov.tr/bulten/index?p=karayolu-trafik-kaza-istatistikleri-2020-37436
CR  - World Health Organization (2019). World Health    Organization, Statistics Annual 2019: Geneva., WHO, Statistics Annual. https://iris.who.int/bitstream/handle/10665/324835/9789241565707-eng.pdf
CR  - Yavuz, A. A., Ergül, B., & Aşık, E. G. (2021). Trafik Kazalarının Makine Öğrenmesi Yöntemleri Kullanılarak Değerlendirilmesi Evaluation of Traffic Accidents Using Machine Learning Methods. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 13(1), 66–73. https://doi.org/10.29137/umagd.705156
UR  - https://doi.org/10.38002/tuad.1572607
L1  - https://dergipark.org.tr/tr/download/article-file/4310946
ER  -