Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study

Abstract

Background:Road traffic accidents and their related deaths have become a major concern, particularly in developing countries. Iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. In this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying ARIMA models on data from Taybad city. Methods: This study is a cross-sectional study. We used data from accidents occurring in Taybad between 2007 and 2011. We obtained the data from the Ministry of Health (MOH) and used the time series method with a time lag of one month. After plotting the trend, non-stationary data in mean and variance were removed using Box-Cox transformation and a differencing method respectively. The ACF and PACF plots were used to control the stationary situation. Results: The traffic accidents in our study had an increasing trend over the five years of study. Based on ACF and PACF plots gained after applying Box-Cox transformation and differencing, data did not fit to a time series model. Therefore, neither ARIMA model nor seasonality were observed. Conclusion: Traffic accidents in Taybad have an upward trend. In addition, we expected either the AR model, MA model or ARIMA model to have a seasonal trend, yet this was not observed in this analysis. Several reasons may have contributed to this situation, such as uncertainty of the quality of data, weather changes, and behavioural factors that are not taken into account by time series analysis.

A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality

“…It is estimated that RTA will become the 6 th leading cause of death in the world and take the third place of disability by the year 2020 (3). Injuries, deaths and disabilities resulting from RTA are considered as a major public health concerns to which inadequate attention has been paid so far (4, 5). …”

“…The burden mortalities, disabilities and injuries due to traffic accidents has a worse effect on health, social and economic developments of many nations, especially low and middle-income countries (2, 4). Injuries due to RTA are one of the main health care problems, which are preventable as the experience of many developed countries (7-9).…”

Forecasting the Trend of Traffic Accident Mortality in West Iran

“…Based on estimates, Iranian road traffic accidents lead to about 30,000 deaths annually (5). This problem has become the main priority for public health policy, and several interventions have been implemented to enhance prevention and control (6). Appropriate planning for these interventions requires methods that effectively assess health events and indicate possible outcomes in future years (7).…”

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

“…In one component the current observation depends on its previous values which are explained by the autoregressive (AR) component of "p" order, whereas the second component, moving average (MA) component of "q" order, is used to describe a time series process as a linear function of current and previous random errors. 13 In case seasonal components are included in the model, then it is called as the seasonal auto regressive integrated moving average (SARIMA) model. A general form of SARIMA model is given as follows,…”