Temporal stability of the factors related to the severity of drivers' injuries in rear-end collisions.

Most of the previous studies that investigated the factors increasing the severity of rear-end collisions were based on analyzing collision reports from multiple years and combining them into a single dataset for analysis. Analyzing pooled data from multiple years carries the risk of introducing aggregation bias in the analysis. Those aggregated models might be structurally unstable, and the significance of the risk factors identified using those aggregated models might change over time due to the ongoing changes in vehicle technologies, law-enforcement technologies, and drivers' attitudes. This study demonstrates the importance of testing the temporal stability of pooled data by utilizing logistic regression modeling to analyze all rear-end collisions that occurred in North Carolina for the period from January 1, 2004 to December 31, 2015. Separate models were developed for each year to model injury severity of striking and struck drivers. The year-wise models were compared together to identify the most temporally stable factors, and it was found that older and female drivers are usually more severely injured, but they do not increase injury severity of the drivers they collide with. It was also found that compared to other light-duty vehicles, passenger cars are usually associated with increased injury severity to their drivers and reduced injury severity to the drivers of the vehicles they collide with. The increased age of a vehicle was found to increase the injury severity of its driver as well as the driver of the vehicle it collides with. Dark conditions were found to increase drivers' injury severity, but adverse weather conditions have no similar effect. For comparison, aggregated models were also developed by pooling data from all analysis years (from 2004 to 2015) and were found to return significant factors that were found by the year-wise models to be temporally unstable. Chow tests were performed on the data, and it was found that pooling data for four years or more generally returned structurally unstable models.