Rear-end collisions represent the most common type of collisions with more than 2.5 million rear-end collisions reported every year in the United States. Most of the previous research studies that identified risk factors related to the probability of injury in rear-end collisions were based on pooling data from few years (usually 2 – 4 years) into a single dataset for analysis. This approach carries the risk of introducing aggregation bias in the analysis. Risk factors identified using that approach might have been significant at the time of the analysis, but their significance might change afterward due to the ongoing changes in vehicle technologies, driver behavior, traffic volumes, road conditions, law-enforcement technologies, and implemented policies. Thus, understanding the temporal stability trends of the factors influencing how drivers are injured in rear-end collisions would help researchers in evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements, observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors related to drivers’ injuries in rear-end collisions. The research is based on utilizing logistic regression modelling to analyze all rear-end collisions that occurred in North Carolina from January 1, 2007 to December 31, 2013. A logistic regression model is developed for each year, and the models are compared together to identify the most temporally stable factors related to drivers’ injuries in rear-end collisions. The findings of this research have the potential to help decision makers develop policies and countermeasures that improve roadway safety by focusing on risk factors that consistently increase the probability of injuries for drivers in rear-end collisions.