Modeling and Analyzing the Accidents Severity due to the Lack of Attention to the Front in Corona Pandemic Restrictions using Logistic Regression

Traffic accidents are one of the main causes of death in the world and the resulting damages have an important effect on the economy of any country that the need to identify factors affecting accidents that lead to a reduction in the frequency or severity of accidents is understandable. Since the outbreak of Covid-19, travel restriction policies have been widely adopted by cities around the world that it played a profound role in changing the shape of urban travel patterns. One of the factors that influenced traffic behavior in recent years was the epidemic of Corona disease in the world. Therefore, new driving rules and regulations were established for urban and suburban traffic, and traffic behaviors were affected by those rules. It is possible to investigate the effect of each factor on the severity of accidents by using accident severity models according to the effective parameters. In this article, urban accidents due to lack of attention during the corona epidemic were analyzed using logistic regression and information related to intra-urban accidents in Rasht city. The final model shows that the independent variables of working day, at-fault male drivers over 60 years of age, front-to-front collision and the at-fault vehicle Pride increase the probability of a fatal or injury accident compared to a damage accident. Variables with negative coefficients (6:00 a.m. to 9:00 p.m., dry road surface conditions, clear weather, and daylight conditions) reduce the probability of fatal or injury accidents compared to damage accidents.