Slime mould algorithm based approaches to solve traffic insurance gross premiums of Türkiye

Abstract

Highway traffic injuries are a major public health problem for all nations. As it is seen in the whole world, also in Türkiye, road traffic crashes are among the ones that cause death. As a result, road traffic congestion and fatalities represent a significant cost to national economies. The compulsory motor vehicle liability insurance is one of the most common types of insurance, both because it is compulsory and because the number of motor vehicles in Türkiye is very high. Therefore, estimation of the traffic insurance gross premiums (TIGP) problem is being an important problem for Türkiye as well as the other countries. In this study, in order to make some proper TIGP estimations for Türkiye, three different SMA methods such as SMA-Linear, SMA-Quadratic and SMA-Exponential are proposed. In the experiments, the population, number of vehicles and number of accidents and the observed TIGP historical data records of Türkiye taken from Turkish statistical institute (TUIK) and Turkish insurance association (TSB) for the years (2009-2020) have been used. First, the models are constructed using the SMA-Linear, SMA-Quadratic and SMA-Exponential methods, and then the methods based on the SMA-Linear, SMA-Quadratic and SMA-Exponential models are used to estimate the TIGP values for the years (2009-2020). According to the experimental results, SMA-Quadratic methods is produced better or comparable performance on the problem dealt with this study in terms of solution quality and robustness.

Keywords

• Exponential model
• linear model
• quadratic model
• slime mould algorithm
• traffic insurance gross premiums problem

References

1. O. Ercan, G. Erhan, K. Zubeyde, Estimation of the costs of traffic accidents in turkey: An evaluation in terms of the insurance and financial system, Yaşar Üniversitesi E-Dergisi, 9 (2014) 5649-5673.
2. H.Y. Keser, S. Ay, İ. Çetin, Ulaştırmada Karayolları: Türkiye’deki Gelecek Beklentileri, TESAM Akademi Dergisi, 5 (2018) 63-93.
3. G. Lindberg, Traffic insurance and accident externality charges, Journal of Transport Economics and Policy (JTEP), 35 (2001) 399-416.
4. M.F. TEFEK, M. ARSLAN, Türkiye’de Trafik Sigorta Primlerinin Harris Şahinleri Algoritması ile Tahmini, Avrupa Bilim ve Teknoloji Dergisi, (2022) 711-715.
5. M.F. Tefek, M. Arslan, Highway accident number estimation in Turkey with Jaya algorithm, Neural Computing and Applications, 34 (2022) 5367-5381. [6]. P. Ong, H.-G. Sung, Exploratory Study of Spatial Variation in Car Insurance Premiums, Traffic Volume and Vehice Accidents, (2003).
6. P. Margaret, R. Scurfield, D. Sleet, D. Mohan, A.A. Hyder, E. Jarawan, C. Mathers, World Report on Road Traffic Injury Prevention, in, https://www.who.int/publications/i/item/world-report-on-road-traffic-injury-prevention, 2004.
7. World Health Organization, Global Status Report on Road Safety: Supporting A Decade of Action, in: World Health Organization., https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries, Geneva, . 2023.
8. A. Temur, Türkiye’de Trafik Sigortalarının Branş Karlılığını Etkileyen Faktörler ve Bu Faktörlerin Sigorta Sektörü Karlılığına Etkisi, Akademik Hassasiyetler, 5 (2018) 305-330.

9. I.I. Institute, Brief history of insurance, in, https://www.iii.org./publications/insurance-handbook/brief-history, 2023.
10. O. ÖZTÜRK, E. Cenker, Motorlu taşıt satışlarının trafik kazaları üzerine olan etkileri, SDÜ Tıp Fakültesi Dergisi, 13 (2006) 12-15.
11. H. PETEK, Kamu tüzel kişilerinin Karayolları Trafik Kanunu’na göre hukuki sorumluluğu, Dokuz Eylül Üniversitesi Hukuk Fakültesi Dergisi, 16 (2014) 3287-3342.
12. E. Kırkbeşoğlu, Risk yönetimi ve sigortacılık, Ankara: Gazi Kitabevi, (2015).
13. M. Çekici, M. İnel, Türk sigorta sektörünün direkt prim üretimlerinin tahmin teknikleri ile incelenmesi, Marmara Üniversitesi İktisadi ve İdari Bilimler Dergisi, 34 (2013) 135-152.
14. M. Aslan, Archimedes optimization algorithm based approaches for solving energy demand estimation problem: a case study of Turkey, Neural Computing and Applications, 35 (2023) 19627-19649.
15. M. Aslan, M. Beşkirli, Realization of Turkey’s energy demand forecast with the improved arithmetic optimization algorithm, Energy Reports, 8 (2022) 18-32.
16. E. Assareh, M. Behrang, M. Assari, A. Ghanbarzadeh, Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran, Energy, 35 (2010) 5223-5229.
17. M. Beekman, T. Latty, Brainless but multi-headed: decision making by the acellular slime mould Physarum polycephalum, Journal of molecular biology, 427 (2015) 3734-3743.
18. M.S. Kıran, E. Özceylan, M. Gündüz, T. Paksoy, A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey, Energy conversion and management, 53 (2012) 75-83.
19. H. Nazari, A. Kazemi, M.-H. Hashemi, M.M. Sadat, M. Nazari, Evaluating the performance of genetic and particle swarm optimization algorithms to select an appropriate scenario for forecasting energy demand using economic indicators: residential and commercial sectors of Iran, International Journal of Energy and Environmental Engineering, 6 (2015) 345-355.
20. M.A. Sahraei, H. Duman, M.Y. Çodur, E. Eyduran, Prediction of transportation energy demand: multivariate adaptive regression splines, Energy, 224 (2021) 120090.
21. H. Uguz, H. Hakli, Ö.K. Baykan, A new algorithm based on artificial bee colony algorithm for energy demand forecasting in Turkey, in: 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), IEEE, 2015, pp. 56-61.
22. M. Aslan, M. Gunduz, M.S. Kiran, JayaX: Jaya algorithm with xor operator for binary optimization, Applied Soft Computing, 82 (2019) 105576.
23. TSB, Türkiye Sigortalar Birliği İstatistikler,Teknik Gelir Tabloları, in, https://www.tsb.org.tr/tr/istatistikler, 2022.
24. National Statistics, http://www.tuik.gov.tr, in, 2022.
25. T. Latty, M. Beekman, Slime moulds use heuristics based on within-patch experience to decide when to leave, The Journal of experimental biology, 218 (2015) 1175-1179.
26. S. Li, H. Chen, M. Wang, A.A. Heidari, S. Mirjalili, Slime mould algorithm: A new method for stochastic optimization, Future Generation Computer Systems, 111 (2020) 300-323.
27. D. Özdemir, S. Dörterler, D. Aydın, A new modified artificial bee colony algorithm for energy demand forecasting problem, Neural Computing and Applications, 34 (2022) 17455-17471.