Evaluating the impact of COVID-19 on traffic congestion and safety skills using structural equation modeling (SEM) and Auto-Regressive Integrated Moving Average (ARIMA)

Abstract

The current work presented a comparative analysis of traffic demand and safety skills before and after control measures during the COVID-19 epidemic, acquired time-series change data curves, and constructed a prediction model after determining the trend of traffic demand over time. From a data analysis perspective, the paper draws some interesting conclusions about long span, coarse sampling studies. In terms of the study population, the paper did focus on the specificity of the global epidemic. Kuwait was selected as a case study. Traffic demand analysis was conducted using a Structural Equation Model (SEM), Auto-Regressive Integrated Moving Average (ARIMA), and safety skills questionnaire along with flow charts and demographic variables. These methods were utilized to study the impact of COVID-19 on traffic congestion and safety skills as well as to forecast the future traffic volumes. Results showed that traffic congestion had a significant reduction during COVID-19 as a result of the preventive safety measures taken to control the spread of the virus. Such reduced traffic volume was associated with a decrease in traffic violations and an increase in the safety skills and PM skills of drivers.

Keywords:

• COVID-19
• traffic congestion
• safety skills
• structural equation modeling (SEM)
• Auto-Regressive Integrated Moving Average (ARIMA)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data used in this work appear in the article and supplementary materials.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.