Building SARIMA Models to Analysis and Forecast Time Series Data of Road Traffic Accidents in the Kurdistan Region of Iraq
DOI:
https://doi.org/10.21271/zjhs.27.1.23Keywords:
SARIMA, ForecastingTraffic AccidentsAbstract
Traffic accidents cause injuries and deaths, but also cause material damage to society. Therefore, predicting and identifying the causes of traffic accidents is necessary and important to reduce these losses. The main objective of this study is to develop an ARIMA time series model to investigate and analyze the number of traffic accidents and the number of deaths in traffic accidents in the Iraqi Kurdistan Region according to monthly during the years (2014-2021) and monthly forecast for 2022. Data were obtained from Erbil General Directorate of Traffic. The results have shown that the series have seasonal characteristics. After testing several models and selecting the best results based on the minimum statistical criteria (RMSE, MAE, MAPE) used for comparison. The best results were we found SARIMA (1,1,1)(0,1,1)12 models for the number of accidents and SARIMA (0,1,1)(1,1,2)12 models for the number of deaths.
Finally, using the best models, we made a monthly forecast for the number of accidents and the number of deaths. We can say that the rate has not significantly decreased for the forecast period, so the government should develop better and more detailed plans to reduce traffic accidents.
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