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During the past several years, the COVID-19 pandemic has had pronounced impacts on traffic safety. Existing studies found that the crash frequency was reduced and the severity level was increased during the earlier "Lockdown" period. However, there is a lack of studies investigating its impacts on traffic safety during the later stage of the pandemic. To bridge such a gap, this study selects Salt Lake County, Utah as the study area and employs statistical methods to investigate whether the impact of COVID-19 on traffic safety differs among different stages. Negative binomial models and binary logit models were utilized to study the effects of the pandemic on the crash frequency and severity respectively while accounting for the exposure, environmental, and human factors. Welch's t-test and Pairwise t-test are employed to investigate the possible indirect effect of the pandemic by influencing other non-pandemic-related factors in the statistical models. The results show that the crash frequency is significantly less than that of the pre-pandemic during the whole course of the pandemic. However, it significantly increases during the later stage due to the relaxed restrictions. Crash severity levels were increased during the earlier pandemic due to the increased traffic speed, the prevalence of DUI, reduced use of seat belts, and increased presence of commercial vehicles. It reduced to a level comparable to the pre-pandemic later, owing to the reduction of speed and increased seat-belt-used to the pre-pandemic level. As for the incoming "New Normal" stage, stakeholders may need to take actions to deter DUI and reduce commercial-vehicle-related crashes to improve traffic safety., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Objective: Few existing studies in the literature devoted efforts to examine the driver injury severity in left-turn crashes. To fill this research gap, this paper aims to provide a comprehensive study of the contributing factors of left-turn crashes and the corresponding injury severities., Methods: The hierarchical ordered probit (HOPIT) model is first applied to study driver injury severity in left-turn crashes. The HOPIT model can overcome the limitations of traditional ordered probit models since its thresholds are always positive and ordered. It is a function of unique explanatory parameters that do not necessarily affect the ordered probability outcomes directly. Considering the driving condition during the wintertime could be significantly different from other seasons, this study divided the overall crash dataset into "winter" and "other-season" subsets based on the temperature, snowing condition, and other factors., Results: With the "other-season" dataset, results demonstrated that contributing factors, such as young drivers, male drivers, clear, light, and ramp intersection with crossroad, are associated with a decrease in injury severity. On the contrary, factors like drug, alcohol, disregard traffic control device, high-speed limit, the protected left-turn signal, etc., are related to an increase in injury severity. In winter, results revealed that only nine contributing factors are significant to the left-turn crash. Alcohol, disregard traffic control device, nighttime, high-speed limit, head-on collision, and state road are associated with an increase in injury severity. Also, two-vehicle involved, snow, ramp intersection with crossroad are related to a decrease in injury severity., Conclusions: The HOPIT model is applied to examine contributing factors of left-turn crashes and the corresponding injury severity, based on left-turn crash records from 2010 to 2017 in Utah. Eighteen significant factors of left-turn crash injury severity are identified in the overall dataset. In seasons rather than winter, the significant factors are almost the same as that of the entire year. In the winter, less significant factors and different patterns are found compared with the overall crashes.