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

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 di...

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Main Authors: Mohammad Reza Omidi, Meysam Jafari Eskandari, Sadigh Raissi, Amir Abbas Shojaei
Format: Article
Language:English
Published: Negah Institute for Scientific Communication 2019-04-01
Series:Health in Emergencies & Disasters Quarterly
Subjects:
Online Access:http://hdq.uswr.ac.ir/article-1-220-en.html
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spelling doaj-891a2928f07e41149c90ac98cde4a5482020-11-25T01:12:24ZengNegah Institute for Scientific CommunicationHealth in Emergencies & Disasters Quarterly2345-42102345-42102019-04-0143165172Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil ProvincesMohammad Reza Omidi0Meysam Jafari Eskandari1Sadigh Raissi2Amir Abbas Shojaei3 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University، Tehran, Iran. Department of Industrial Engineering, Payame Noor University, Tehran, Iran. Department of Industrial Engineering, South Tehran Branch, Islamic Azad University، Tehran, Iran. Department of Industrial Engineering, South Tehran Branch, Islamic Azad University، Tehran, Iran. 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 re-planning to reduce accidents there.http://hdq.uswr.ac.ir/article-1-220-en.htmlepidemiologyaccidentstransportation
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Reza Omidi
Meysam Jafari Eskandari
Sadigh Raissi
Amir Abbas Shojaei
spellingShingle Mohammad Reza Omidi
Meysam Jafari Eskandari
Sadigh Raissi
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 & Disasters Quarterly
epidemiology
accidents
transportation
author_facet Mohammad Reza Omidi
Meysam Jafari Eskandari
Sadigh Raissi
Amir Abbas Shojaei
author_sort Mohammad Reza Omidi
title Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces
title_short Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces
title_full Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces
title_fullStr Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces
title_full_unstemmed Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces
title_sort providing an appropriate prediction model for traffic accidents: a case study on accidents in golestan, mazandaran, guilan, and ardebil provinces
publisher Negah Institute for Scientific Communication
series Health in Emergencies & Disasters Quarterly
issn 2345-4210
2345-4210
publishDate 2019-04-01
description 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 re-planning to reduce accidents there.
topic epidemiology
accidents
transportation
url http://hdq.uswr.ac.ir/article-1-220-en.html
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