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

Authors

  • 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

DOI:

https://doi.org/10.21271/zjhs.28.1.18

Keywords:

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

Abstract

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.

References

- ALN. A. 2019. Using multivariate time series analysis to study highway traffic accidents in Sudan.. PhD Thesis, University of Science and Technology, Sudan.

- Al-Zawbaee, O.M. and Mahmood, N.H., 2023. Building an Efficient Model by Using Panel Data. Cihan University-Erbil Journal of Humanities and Social Sciences, 7(1), pp.82-87.

- Alzawbaee, O. M., Hamad, A. P. D. A. A., & Mahmood, A. L. A. O. 2020. Using Time Series Analysis to Forecast the prevalence of the COVID-19 Pandemic (Iraq and Gulf Cooperation Council countries (GCC)).

- BJERRE, J., KIRKEBJERG, P. G. & LARSEN, L. B. 2006. Prevention of traffic deaths in accidents involving motor vehicles. Ugeskrift for Læger, 168, 1764-1768.

- CHAMLIN, M. B. & SANDERS, B. A. 2018. Social policy and crash fatalities: a multivariate time series analysis. Journal of crime and justice, 41, 322-333.

- DICKEY, D. A. & FULLER, W. A. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.

- DING, G., LI, X., JIAO, F. & SHEN, Y. 2020. Brief Analysis of the ARIMA model on the COVID-19 in Italy. medRxiv.

- DOORNIK, J. A. & HANSEN, H. 2008. An omnibus test for univariate and multivariate normality. Oxford bulletin of economics and statistics, 70, 927-939.

- ELLIOTT, G., ROTHENBERG, T. J. & STOCK, J. H. 1992. Efficient tests for an autoregressive unit root. National Bureau of Economic Research Cambridge, Mass., USA.

- ENGLE, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.

- FANELLI, D. & PIAZZA, F. 2020. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134, 109761.

- FARAG, S. G., HASHIM, I. H. & EL-HAMRAWY, S. A. 2014. Analysis and assessment of accident characteristics: Case study of Dhofar Governorate, Sultanate of Oman. Int J Traffic Trans Eng, 3, 189-98.

- FENG, M., WANG, X. & QUDDUS, M. 2020. Developing multivariate time series models to examine the interrelations between police enforcement, traffic violations, and traffic crashes. Analytic methods in accident research, 28, 100139.

- GETAHUN, K. A. 2021. Time series modeling of road traffic accidents in Amhara Region. Journal of big data, 8, 1-15.

- Global status report on road safety. Accident: WHO; 2020.

- https://www.worldlifeexpectancy.com/iraq-road-traffic-accidents

- HE, Z. & TAO, H. 2018. Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study. International Journal of Infectious Diseases, 74, 61-70.

- KADIR, D. 2020. Time Series Modeling to Forecast on Consuming Electricity: A case study Analysis of electrical consumption in Erbil City from 2014 to 2018. Journal of Al-Rafidain University College For Sciences (Print ISSN: 1681-6870, Online ISSN: 2790-2293), 472-485.

- Khidir, H.A., Etikan, İ., Kadir, D.H., Mahmood, N.H. and Sabetvand, R., 2023. Bayesian machine learning analysis with Markov Chain Monte Carlo techniques for assessing characteristics and risk factors of Covid-19 in Erbil City-Iraq 2020–2021. Alexandria Engineering Journal, 78, pp.162-174.

- LJUNG, G. M. & BOX, G. E. 1978. On a measure of lack of fit in time series models. Biometrika, 65, 297-303.

- Mahmood, A. L. A. O & AL-Takriti, M. S. 2013. A New Suggested Technique to Determine the Best Models of Time Series. Damascus University Journal for Basic Sciences, V (29).

- MEIßNER, K. & RIECK, J. 2021. Multivariate Forecasting of Road Accidents Based on Geographically Separated Data. Vietnam Journal of Computer Science, 8, 433-454.

- MOHAMMED, P. A., OBED, S. A., ALI, I. M. & KADIR, D. H. 2022. US Dollar/IQ Dinar Currency Exchange Rates Time Series Forecasting Using ARIMA Model. Cihan University-Erbil Scientific Journal, 6, 12-19.

- PARVAREH, M., KARIMI, A., REZAEI, S., WOLDEMICHAEL, A., NILI, S., NOURI, B. & NASAB, N. 2018. Assessment and prediction of road accident injuries trend using time-series models in Kurdistan', Burns & Trauma 6 (1), 9.PEDEN, M. 2005. Global collaboration on road traffic injury prevention. International journal of injury control and safety promotion, 12, 85-91.

- PERONE, G. 2020. An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy. MedRxiv.

- SMART, R. G. & MANN, R. E. 2002. Deaths and injuries from road rage: cases in Canadian newspapers. CMAJ, 167, 761-762.

- SMITH, R. L., WIGLEY, T. M. & SANTER, B. D. 2003. A bivariate time series approach to anthropogenic trend detection in hemispheric mean temperatures. Journal of Climate, 16, 1228-1240.

Downloads

Published

2024-02-15

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. https://doi.org/10.21271/zjhs.28.1.18

Issue

Vol. 28 No. 1 (2024)

Section

Articles

License

Copyright (c) 2024 Nozad Hussein Mahmood , Dler Hussen Kadir , Obaid Mahmud Mohsin Alzawbaee

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Except where otherwise noted, content on this site is licenced
under a  Creative Commons Attribution License 4.0 (CC BY- 4.0)