Predicting Number of Traumas Using the Seasonal Time Series Model
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Abstract:
Background

Road accidents and casualties resulted are among the current challenges of human societies, which have imposed a high cost on the economies of countries.

Objectives

Prediction of accidents caused by driving incidents helps planners achieve a suitable model to reduce the occurrence of traumas resulted from the driving accidents.

Materials and Methods

In this study, a seasonal time series model was used for predicting the number of road accident traumas. Data related to the patients referring to Imam Khomeini Hospital in Ilam Province were evaluated from March 2012 to June 2017.

Results

The results showed that during November and October in 2015 and 2016, we had the highest number of accidents due to high traffic during New Year’s Vacation, summer trips, and religious pilgrimages including the Arbaeen. Moreover, the results depicted that the seasonal Arima model was effective in predicting the number of traumas due to accidents. Furthermore, forecasting the model showed an ascending trend in the number of accidents in the following 3 years.

Conclusion

The number of accident traumas in the forthcoming months can be predicted through time series models. Of course, these models can be used by managers as appropriate tools for traffic analysis. Furthermore, the increasing trend in the number of traumas indicates that serious consideration for planning and managing such events seems necessary for the administrators in the near future.

Article Type:
Research/Original Article
Language:
English
Published:
Archives of Trauma Research, Volume:8 Issue:1, 2019
Pages:
23 - 27
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