Use of ARIMA Model for Forecasting Consequences Due to Traffic Crashes in the Kingdom of Saudi Arabia
Times series models are important statistical methods for analysing data recorded at points of time which considers the order of observations. In this study, the Autoregressive Integrated Moving Average (ARIMA) model was used to analyse the consequences of traffic crashes in the Kingdom of Saudi Ara...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Australasian College of Road Safety
2024-11-01
|
| Series: | Journal of Road Safety |
| Online Access: | https://doi.org/10.33492/JRS-D-24-4-2400749 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Times series models are important statistical methods for analysing data recorded at points of time which considers the order of observations. In this study, the Autoregressive Integrated Moving Average (ARIMA) model was used to analyse the consequences of traffic crashes in the Kingdom of Saudi Arabia (KSA) from 2002-2022. Over the study period, there was a decreasing trend in the forecasted number of all types of injuries per 1,000 traffic crashes. Moreover, to check the validity of the fitted model, the actual observations are plotted with predicted values from 2016 to 2022 and showed a nearly equal and exact pattern between the total number of predicted values and the actual data. It is concluded that the ARIMA model is a good fit to forecast the parameter of consequences per 1,000 crashes. The decrease in consequences may be due to preventive or mitigation measures by various organisations in KSA. |
|---|---|
| ISSN: | 2652-4260 2652-4252 |