Building a Statistical Model to Forecast Traffic Accidents for Death and Injuries by Using Bivariate Time Series Analysis


  • Nozad Hussein Mahmood Department of Business Administration, Cihan University Sulaimaniya, 46001, Kurdistan region, Iraq
  • Dler Hussen Kadir Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University-Erbil Kurdistan Region, Iraq Department of Business Administration, Cihan University-Erbil, Kurdistan Region, Iraq
  • Obaid Mahmud Mohsin Alzawbaee Department of Business Administration, Cihan University Sulaimaniya, 46001, Kurdistan region, Iraq



Multivariate time series, VARMA (p, q), Forecasting, Traffic Accident.


In our study, multivariate time series were used that included two variables, namely, the death and injury rates from car accidents in Erbil City Iraq. The data for the two series were collected monthly from January 2015 to December 2020, so there are 72 units in each series. The most important finding is that the time series is stationary, and the appropriate model to represent the phenomenon studied is VARMA (1,0). A statistical model was adopted to forecast accidents resulting in death and injuries for 2024, and it was found to be appropriate. Furthermore, we use R-programing and STATA version 17 to analyze our data. As a result, the study suggested that the Iraqi Kurdistan Traffic Department could use the model developed to forecast the phenomenon's future trends.

Author Biography

Nozad Hussein Mahmood , Department of Business Administration, Cihan University Sulaimaniya, 46001, Kurdistan region, Iraq

Nozad H. Mahmood is currently a full-time lecturer and the Head of the Business Administration Department at Cihan University-Sulaimaniya. He is also the director of statistical consulting for data analysis and training. He received his MS degree in Statistical Computing from the University of Central Florida in the US and a B.Sc. in Statistics from Salahaddin University-Erbil. His scholarly interests and expertise include variable selection, regularized regression, clustering, data mining and classification, dimension reduction, categorical data analysis, and experimental design.


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How to Cite

Mahmood , N. H., Kadir , D. H. ., & Alzawbaee , O. M. M. . (2024). Building a Statistical Model to Forecast Traffic Accidents for Death and Injuries by Using Bivariate Time Series Analysis. Zanco Journal of Human Sciences, 28(1), 278–289.