Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model

Authors

  • Manikandan M. Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India
  • Vishnu Prasad R. Department of Health, Government of India, New Delhi, India
  • Amit Kumar Mishra Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India
  • Rajesh Kumar Konduru Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India
  • Newtonraj A. Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India

DOI:

https://doi.org/10.18203/2394-6040.ijcmph20183579

Keywords:

Univariate time series, SARIMA, AIC, BIC, RTA, Forecasting

Abstract

Background: As per World Health Organization (WHO) report 1.24 million people die each year as a result of road traffic accidents (RTA) globally. A vast majority of 20-50 million people suffer from non-fatal injuries, many of them ultimately end in disability. Forecasting RTA deaths could help in planning the intervention at the right time in an effective way.

Methods: An attempt was made to forecast the RTA deaths in India with seasonal auto regressive integrated moving average (SARIMA) model. ARIMA model is one of the common methods which are used for forecasting variables as the method is very easy and requires only long time series data. The method of selection of appropriate ARIMA model has been explained in detail. Month wise RTA deaths for previous years data was collected from Govt. of India website. Data for 12 years (2001 to 2012) was extracted and appropriate ARIMA model was selected. Using the validated ARIMA model the RTA deaths are forecasted for 8 years (2013-2020).

Results: The appropriate SARIMA (1,0,0) (2,1,0) 12 model was selected based on minimal AIC and BIC values. The forecasted RTA deaths show increasing trend overtime.

Conclusions: There is an increasing trend in the forecasted numbers of road traffic accidental deaths and it also shows seasonality of RTA deaths with more number of accidents during the month of April and May in every years. It is recommended that the policy makers and transport authority should pay more attention to road traffic accidents and plan some effective intervention to reduce the burden of RTA deaths.

Author Biographies

Manikandan M., Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India

Assistant Professor cum Biostatistician,
Department of Community Medicine,
Pondicherry Institute of Medical Sciences,
Puducherry.

Vishnu Prasad R., Department of Health, Government of India, New Delhi, India

Specialist- Preventive and Social Medicine

Department of Health

Government of India

New Delhi, India

Amit Kumar Mishra, Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India

Dr Amit Kumar Mishra, Assistant Professor, Department of Community Medicine, Pondicherry Institute of Medical Sciences, Kalapet, Pondicherry. 605014

Rajesh Kumar Konduru, Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India

Professor,

Department of Community Medicine,

Pondicherry Institute of Medical Sciences,

Puducherry.

Newtonraj A., Department of Community Medicine, Pondicherry Institute of Medical Sciences, Puducherry, India

Assistant Professor,

Department of Community Medicine,

Pondicherry Institute of Medical Sciences,

Puducherry.

References

World Health Organization. Global Status report on road safety. Geneva: WHO; 2015.

World Health Organization. World Health Statistics 2017. Geneva: WHO; 2017.

National Crime Records Bureau Report: Accidental Deaths and Suicide in India, 2014. New Delhi; Government of India.

Shumway RH. Applied Statistical Time Series Analysis. In: Englewood Cliffs, NJ: Prentice-Hall; 1988.

Goel R. Modelling of road traffic fatalities in India. Accident Analysis and Prevention. 2018;112:105–15.

Shahrokh YC, Fatemeh RT, Reza M, Alireza R. A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality. Arch Trauma Res. 2016;5(3):36570.

Dalbir S, Satinder P. Singh, Kumaran M, Sonu Goel. Epidemiology of road traffic accident deaths in children in Chandigarh zone of North West India. Egyptian J Forensic Sci. 2016;6:255–60.

Mutang K. Time Series Analysis of Road Traffic Accidents in Zimbabwe. Int J Statistics Applications. 2015;5(4):141-9.

Box GEP, Jenkins G. Time Series Analysis, Forecasting and Control. San Francisco, CA: Holden Day; 1970.

Open Government Data (OG). Available at: https://data.gov.in/catalog/stateut-wise-details-road-accident-deaths-mode-transport. Accessed on 2 February 2018.

Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50:159-75.

Zhang X, Pang Y, Mengjing C, Lorann S,Huiyun X. Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Annals Epidemiol. 2015;25(2):101-6.

Razzaghi A, Bahrampour A, Baneshi MR, Zolala F. Assessment of trend and seasonality in road accident Data: An Iranian case study. Int J Heal Policy Manag. 2013;1(1):51-5.

Downloads

Published

2018-08-24

How to Cite

M., M., R., V. P., Mishra, A. K., Konduru, R. K., & A., N. (2018). Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model. International Journal Of Community Medicine And Public Health, 5(9), 3962–3968. https://doi.org/10.18203/2394-6040.ijcmph20183579

Issue

Section

Original Research Articles