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Information Journal Paper

Title

Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces

Pages

  165-172

Abstract

 Background: Road traffic Accidents in Iran are a critical issue that hinders economic development and one of the main threats to the health and safety of people in the community. The statistics indicate that after cardiovascular diseases, traffic Accidents are the second leading cause of death in different age groups, which reflects the necessity of prediction in this area. Materials and Methods: The present study investigated the data of the traffic-accident injured people between April 2009 and March 2012 in Golestan, Mazandaran, Guilan, and Ardebil provinces, presented to forensic medicine. We used the Box-Jenkins method as one of the most advanced methods in prediction and future studies in the field of health systems, to estimate the number of injuries by province, for the years 2016 to 2019. Results: The obtained results suggested the appropriate time series patterns for predicting injured people in Golestan Province with Autoregressive Integrated Moving Average (ARIMA) (4, 2, 4), Mazandaran Province with ARIMA (3, 1, 5), Guilan Province with ARIMA (3, 1, 4), and Ardabil Province with ARIMA (5, 1, 2). Furthermore, the mean percentages of absolute error for different provinces were as follows: Golestan Province, 0. 114; Mazandaran Province, 0. 064; Guilan Province, 0. 078; and Ardabil Province, 0. 1250. These data demonstrate the high precision of the Box-Jenkins method in predicting the number of traffic-accident injured people, especially in Mazandaran and Guilan provinces. Estimated values for 2016 to 2019 indicate that the road traffic injuries are increasing in Golestan Province and decreasing in Mazandaran, Guilan, and Ardebil provinces. Conclusion: The high precision of the Box– Jenkins method makes it an appropriate way for experts and authorities to predict traffic accident injuries in Golestan, Guilan, Mazandaran, and Ardebil provinces. The reduced number of casualties in Mazandaran, Guilan, and Ardebil indicate a progressive improvement in the Transportation system conduct in these provinces. Moreover, Golestan Province is moving towards an increase in traffic Accidents, requiring replanning to reduce Accidents there.

Cites

References

Cite

APA: Copy

OMIDI, MOHAMMAD REZA, JAFARI ESKANDARI, MEYSAM, RAISSI, SADIGH, & Shojaei, Amir Abbas. (2019). Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces. HEALTH IN EMERGENCIES AND DISASTERS QURTERLY, 4(3), 165-172. SID. https://sid.ir/paper/346097/en

Vancouver: Copy

OMIDI MOHAMMAD REZA, JAFARI ESKANDARI MEYSAM, RAISSI SADIGH, Shojaei Amir Abbas. Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces. HEALTH IN EMERGENCIES AND DISASTERS QURTERLY[Internet]. 2019;4(3):165-172. Available from: https://sid.ir/paper/346097/en

IEEE: Copy

MOHAMMAD REZA OMIDI, MEYSAM JAFARI ESKANDARI, SADIGH RAISSI, and Amir Abbas Shojaei, “Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces,” HEALTH IN EMERGENCIES AND DISASTERS QURTERLY, vol. 4, no. 3, pp. 165–172, 2019, [Online]. Available: https://sid.ir/paper/346097/en

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