Predicting economic indices of vehicle insurance using the “Grey-System Theory”

• Autorzy: Opoka Kazimierz

Identyfikatory

DOI

10.20858/sjsutst.2020.107.9

• Języki publikacji: EN

Abstrakty

EN

This paper contains a prognosis of vehicle insurance economic indices using the Grey System Theory. It has been prepared based on data provided by a certain insurance company. The following economic factors were analysed: number of insured vehicles, premiums income, amount of damage cases covered, and value of paid compensations. The results of this study indicate a reduction in all analysed indices over twelve months.

Słowa kluczowe

EN

transport; grey system theory; insurance

PL

transport; teoria szarego systemu; ubezpieczenie

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2020
• Tom: z. 107
• Strony: 119--133
• Opis fizyczny: Bibliogr. 14 poz.

Twórcy

autor

Opoka Kazimierz

• State Higher Vocational School in Nowy Sącz, 1a Zamenhofa Street, 33-300 Nowy Sącz, Poland

Bibliografia

• 1. Bodziony M., T. Kądziołka, A. Kochanek, S. Kowalski. 2016. “Work time analysis of drivers in the aspect of road traffic safety”. Autobusy: technika, eksploatacja, systemy transportowe 6: 85-94. ISSN 1509-5878.
• 2. Kądziołka T., S. Kowalski. 2016. “Analysis of road traffic safety exemplified by selected road crossings”. Autobusy: technika, eksploatacja, systemy transportowe 12: 235-241. ISSN 1509-5878.
• 3. Kowalski S., A. Zwolenik. 2019. “Analysis of the company's operating costs on the example of a transport company”. Autobusy: technika, eksploatacja, systemy transportowe 6: 311-314. ISSN 1509-5878.
• 4. Bodziony M., T. Kądziołka, A. Kochanek, S. Kowalski. 2016. “Work time analysis of drivers in the aspect of road traffic safety”. Autobusy: technika, eksploatacja, systemy transportowe 6: 85-94. ISSN 1509-5878.
• 5. Kądziołka T., A. Kochanek, S. Kowalski, 2016. “Cost analysis of fuel consumption by motor vehicles”. Prace Naukowe Politechniki Warszawskiej. Transport 112: 175-183. ISSN 1230-9265.
• 6. Wyborkierowcow.pl. “The worst and best insurance companies in 2019”. Available at: https://www.wyborkierowcow.pl/najgorsze-i-najlepsze-firmy-ubezpieczeniowe-w-2019-roku/.
• 7. „Knowledge base - Statistics”. Available at: https://www.krbrd.gov.pl/.
• 8. Wang S., P. Wang, Y. Zhang. 2020. “A prediction method for urban heat supply based on grey system theory”. Sustainable Cities and Society 52: 1-7.
• 9. Anton Bezuglov A., G. Comert. 2016. “Short-term freeway traffic parameter prediction: Application of grey system theory models”. Expert Systems with Applications 62: 284-292.
• 10. Hosse René S., U. Becker, H. Manz. 2016. “Grey systems theory time series prediction applied to road traffic safety in Germany”. IFAC-PapersOnLine 49(3): 231-236.
• 11. Jui-Chen Huang. 2011. “Application of grey system theory in telecare”. Computers in Biology and Medicine 41: 302-306.
• 12. Ma F.Y., W.H. Wang. “Prediction of pitting corrosion behavior for stainless SUS 630 based on grey system theory”. Materials Letters 61: 998-1000.
• 13. Alfaro-Saiz J.J., M.C. Bas, V.G. Bosch, R. Rodríguez-Rodríguez, M.J. Verdecho. “An evaluation of the environmental factors for supply chain strategy decisions using grey systems and composite indicators”. Applied Mathematical Modelling: 1-16
• 14. Tabaszewski M. 2014. “Prediction of diagnostic symptom values using a set of models GM(1,1) and a moving window method”. Diagnostyka 15(3): 65-68.