The Impact of COVID-19 on Road Traffic Accidents: A Case Study of Severely affected City in China

Abstract

People's travel has changed greatly under the impact of COVID-19. However, it is controversial that whether traffic restrictions of COVID-19 have a positive or negative impact on traffic accidents. At present, there are few studies on the variations of traffic accidents under the impact of COVID-19 in China, and quantitative analysis is rare. Therefore, this study explores the traffic accidents characteristics of W city seriously affected COVID-19. Based on wavelet transform, traffic accident prediction model is established using property damage only accidents data to predict accident frequency without the impact of COVID-19. Compared with the actual traffic accidents frequency, this paper quantitatively analyzes the impact of COVID-19 on traffic accident. The results show that traffic accidents show a trend of decline-bottom-recovery; the frequency of accidents after the recovery is more than the previous year's level; compared with other periods in 2020, the proportion of injury accidents increased sharply during the period when traffic restrictions were gradually loose. The result of accident prediction shows that BP neural network has the best prediction effect. After the implementation of traffic restrictions, the frequency of accidents shows three stages: rapid decline, bottom and continuous rise. In the three stages, the frequency of property damage only accidents decreased by 379.06, 654.72 and 288.19 per day on average.