Forecasting Road Accidental Deaths in India: An Explicit Comparison between ARIMA and Exponential Smoothing Method

Abstract

Abstract The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. With the use of statistical methods and models it is possible to predict the future scenario of deaths due to road accidents with the available data. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-31 based on the data available from 1990-2021. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the 2020 annual report of Ministry of road Transport and Highways, India and ADSI-2021 report of National Crime Record Bureau, Ministry of Home Affairs, India. After examining all the probable models, it is observed that ARIMA (2,2,2) model and exponential smoothing state space model (M, A, N) are suitable for the given data set. Further, the study also shows that forecasted number of deaths for the upcoming 10 years from the proposed models also reveals an upward trend is described.