COVID-19 확산 및 방역지침 변화에 따른 지역별 교통사고시계열분석

초록

The World Health Organization declared COVID-19 a pandemic due to its rapid spread and global impact. Vaccines and treatments remain limited. Countries have prioritized nonpharmaceutical measures such as quarantine and containment to reduce virus transmission. COVID-19, which spreads predominantly in areas with high population density and mobility, has led to societal changes that have impacted road safety. Transportation research on the transmission of infectious diseases is actively conducted abroad, however, domestic research in this area remains scarce. Therefore, the aim of this study was to verify the relationship between the domestic COVID-19 pandemic and transportation. Then, time series prediction techniques are employed to forecast traffic crashes. This study conducted a correlation analysis to establish the link between the count of confirmed COVID-19 cases, transportation indicators reflecting population movement, and the frequency of traffic crashes. Then, ARIMA, a statistical theorybased technique, and LSTM, a heuristic approach, were employed as time series traffic crash prediction methods. As a result of the analysis, the mean average percentage error of LSTM was 15.08%, which showed that the prediction performance was superior to the statistical theory-based technique. In addition, through SHAP, an explainable artificial intelligence technique, it was confirmed that public transportation usage and highway truck traffic volume have a positive impact on regional time series crash prediction. Based on the study findings, the results can serve as a numeric benchmark to comprehend the influence of external elements, including extremely contagious diseases, on road safety, anticipate traffic incidents, and put into effect suitable safety measures in upcoming times.

키워드