Covid-19 has influenced traffic patterns due to restrictions on travel. During COVID-19 restrictions, many people opted to travel by private cars for their mandatory trips instead of using public transportation, while many non-essential trips were cancelled. The changes in travel restrictions have influenced road safety outcomes. Utilizing decision tree modelling, the current research seeks to evaluate the impact of the COVID-19 pandemic on the severity of road traffic crashes, considering two severity levels: fatal and non-fatal crashes in Iran's Khorasan Razavi province. The analysis considers the time period from May 2019 to October 2021 and includes three main intervals: the pre-COVID-19, strict lockdown period, and post-strict lockdown period. A spatial–temporal analysis of crash data was performed utilizing a data fusion approach with results presented using a Classification and Regression Tree (CART) model. The data fusion approach uses a variety of data sources in order to obtain a thorough overview of the phenomenon being studied. In this study, independent descriptive factors, such as geometric design, weather, temporal variables, driver behavior, crash type, and traffic attributes, were included in the analysis. A similar pattern for crash severity was observed during the pre-COVID-19 and post-strict lockdown phases, with the majority of crashes being non-fatal. However, fatal crashes were more prevalent during the strict lockdown period. Among the independent variables associated with fatalities were traffic volume. In this study we identified that higher traffic volumes resulted in a higher crash frequency, however they were also associated with lower speeds, resulting in a decline in the severity of crashes. Other variables included lack of awareness as a driver behavior and cloudiness as a weather condition.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)