Volume 3, Issue 4 (Summer 2018 -- 2018)
Health in Emergencies and Disasters Quarterly 2018, 3(4): 191-198 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Omidi N, Omidi M R. Estimating Accident-Related Traumatic Injury Rate by Future Studies Models in Semnan Province, Iran. Health in Emergencies and Disasters Quarterly 2018; 3 (4) :191-198
URL: http://hdq.uswr.ac.ir/article-1-173-en.html
URL: http://hdq.uswr.ac.ir/article-1-173-en.html
1- Department of Management, ،Tehran Branch, Payame Noor University, Tehran, Iran.
2- Department of Industrial Engineering, North Tehran Branch, Payame Noor University, Tehran, Iran. ,mromidi_91@yahoo.com
2- Department of Industrial Engineering, North Tehran Branch, Payame Noor University, Tehran, Iran. ,
Abstract: (9305 Views)
Background: Any accident is a disturbance in the balance between the human system, vehicle, road and environment. Future prediction of traumatic accidents is a valuable factor for managers to make strategic decisions in the areas of safety, health and transportation.
Materials and Methods: In this study, by using Grey Model (GM) (1.1), Rolling Grey Model (RGM), Fourier Grey Model (FGM) (1.1), survival modification model, ARIMA time series, harmonic pattern and statistical data, the number of traffic injuries referred to forensic medicine centers in Semnan Province between 2017 and 2020 were predicted based on the number of traffic injured in Semnan Province from March 2009 and March 2016 .
Results: The mean absolute error percentage for the GM (1.1), RGM (1), FGM (1.1), survival model, ARIMA and harmonic models were 0.994, 0.082, 0.091, 0.105, 0.05, 0.11, respectively, indicating a greater accuracy of the ARIMA method, compared to the other methods. The number of road traffic injuries in Semnan Province is decreasing and will reach 4052 in 2020.
Conclusion: ARIMA model is the best method of the future studies model for the number of injured patients referred to the forensic medicine centers in Semnan Province compared to other studied methods. Future studies model shows that the injuries caused by accidents in the province of Semnan are decreasing
Materials and Methods: In this study, by using Grey Model (GM) (1.1), Rolling Grey Model (RGM), Fourier Grey Model (FGM) (1.1), survival modification model, ARIMA time series, harmonic pattern and statistical data, the number of traffic injuries referred to forensic medicine centers in Semnan Province between 2017 and 2020 were predicted based on the number of traffic injured in Semnan Province from March 2009 and March 2016 .
Results: The mean absolute error percentage for the GM (1.1), RGM (1), FGM (1.1), survival model, ARIMA and harmonic models were 0.994, 0.082, 0.091, 0.105, 0.05, 0.11, respectively, indicating a greater accuracy of the ARIMA method, compared to the other methods. The number of road traffic injuries in Semnan Province is decreasing and will reach 4052 in 2020.
Conclusion: ARIMA model is the best method of the future studies model for the number of injured patients referred to the forensic medicine centers in Semnan Province compared to other studied methods. Future studies model shows that the injuries caused by accidents in the province of Semnan are decreasing
Type of Study: Research |
Subject:
General
Received: 2017/12/10 | Accepted: 2018/04/5 | Published: 2018/07/1
Received: 2017/12/10 | Accepted: 2018/04/5 | Published: 2018/07/1
References
1. Zohrevandi BAP, Monsef Kasmaee V, Tajik H, Ashouri A, Ebrahimi H. [Epidemiology of motor cycle accidents in Rasht, 2011- 2012 (Persian)]. Iranian Journal of Forensic Medicine. 2014; 20(4):169-70.
2. Bhalla K, Harrison JE. GBD- 2010 overestimates deaths from road injuries in OECD countries: New methods perform poorly. International Journal of Epidemiology. 2015; 44(5):1648-56. [DOI:10.1093/ije/dyv019] [PMID] [DOI:10.1093/ije/dyv019]
3. World Health Organization. Global status report on road safety: Time for action. Geneva: World Health Organization; 2016.
4. Tavakkoli L, Khanjani N. [The pattern of road crashes emphasizing the factors involved in their occurrence in Kerman city 2012-2015. Journal of Safty Promotion Injury Prevention. 2012; 4(2):101-8.
5. Peden SR, Sleet D, Mohan D, Hyder AA, Jarawan E, Mathers C. World report on road traffic injury prevention. Geneva: World Health Organization; 2004.
6. Chang CJ. Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Safety Science. 2013; 51(1):17-22. [DOI:10.1016/j.ssci.2012.06.017] [DOI:10.1016/j.ssci.2012.06.017]
7. Soni KS, Parmar KS, Kaskaoutis DG. Statistical analysis of aerosols over the gangetichimalayan region using ARIMA model based on long-term MODIS observations. Atmospheric Research. 2014; 149:174-92. [DOI:10.1016/j.atmosres.2014.05.025] [DOI:10.1016/j.atmosres.2014.05.025]
8. Sahebi S, Mahpour A, Norouz A. [The prediction model for the severity of pedestrian traffic accidents in the out-of-town ways (Persian)]. Journal of Transportation Engineering. 2016; 6(4):581-92.
9. Kazemi M, Safarzadeh M, Movagari H, Fallah Zavareh M. [Estimated cost of deducted traffic accidents in Iran (Persian)]. Journal of Transportation Engineering. 2016; 6(4):627-40.
10. Barba L, Rodríguez N, Montt C. Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents. The Scientific World Journal. 2014; 20(14):127-42. [DOI:10.1155/2014/152375] [DOI:10.1155/2014/152375]
11. Razzaghi BA, Baneshi MR, Zolala F. [Assessment of trend and seasonality in road accident data: An Iranian case study (Persian)]. The Scientific World Journal. 2013; 1(1):51-5. [DOI:10.15171/ijhpm.2013.08] [PMID] [PMCID] [DOI:10.15171/ijhpm.2013.08]
12. Zhang ZS, Wang P, Qin Y, Wang H. Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan. Journal of the Air & Waste Management Association. 2017; 67(7):776-88. [DOI:10.1080/10962247.2017.1292968] [DOI:10.1080/10962247.2017.1292968]
13. Li Q, Guo NN, Han ZY, Zhang YB, Qi SX, Xu YG, et al. Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. The American Journal of Tropical Medicine and Hygiene. 2012; 87(2):364-70. [DOI:10.4269/ajtmh.2012.11-0472] [PMID] [PMCID] [DOI:10.4269/ajtmh.2012.11-0472]
14. Purwanto EC, Logeswaran R. An enhanced hybrid method for time series prediction using linear and neural network models. Applied Intelligence. 2012; 37(4):511-9. [DOI:10.1007/s10489-012-0344-1] [DOI:10.1007/s10489-012-0344-1]
15. Xing W, ZiJun D, Bin SH. Comparison of three models on prediction of incidence of pulmonary tuberculosis in Beijing. Beijing Medical Journal. 2010; 9:14.
16. Naghibi SA, Pourghasemi HR, Dixon B. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment. 2016; 188(1):44. [DOI:10.1007/s10661-015-5049-6] [PMID] [DOI:10.1007/s10661-015-5049-6]
17. Wang CH. Predicting tourism demand using fuzzy time series and hybrid grey theory. Tourism Management. 2004; 25(3):367-74. [DOI:10.1016/S0261-5177(03)00132-8] [DOI:10.1016/S0261-5177(03)00132-8]
18. Trung DQ, Ahn KK. Wave prediction based on a Modeling Grey Model MGM(1,1) for real-time control of wave energy converters in irregular wave. Renewable Energy. 2012; 43:242-55. [DOI:10.1016/j.renene.2011.11.047] [DOI:10.1016/j.renene.2011.11.047]
19. Xu J, Tan T, Tu M, Qi L. Improvement of grey models by least squares. Expert Systems With Applications. 2011; 38(11):13961-66. [DOI:10.1016/j.eswa.2011.04.203]
20. Wen YP, Deng Z, Liu K, Zhang Y, Liu L. [Time-series analysis on road traffic injury in China (Chinese)]. Sichuan Da Xue Xue Bao Yi Xue Ban. 2005; 36(6):866-9. [PMID]
21. Yan-Hong L, Rahim Y, Wei L, Gui-Xiang S, Yu Y, Zhou DD, et al. Field data: A study on trend and prediction of fatal traffic injuries prevalence in Shanghai. Traffic Injury Prevention. 2006; 7(4):403-17. [DOI:10.1080/15389580600943336] [PMID] [DOI:10.1080/15389580600943336]
22. Omidi AH, Omidi MR. [Forecasting accidents in transportation systems by using Harmonic, Arch, Dynamic and Temporal patterns (case study of traffic accident victims in Khuzestan province) (Persian)]. Rahvar Research Studies. 2017; 20(1):165-91.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Copyright © The Author(s);
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
