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

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.