Field observations suggest that the Lane-Changing (LC) processes are often interrupted by the surrounding vehicles. Understanding these intermediate interruptions and evasive behavior during an LC is necessary for developing realistic simulation tools, collision-warning systems, and human-like LC in self-driving vehicles. However, identifying and classifying LC into interrupted (ILC) and uninterrupted (ULC) is not straightforward. This study proposes a methodology for the automatic identification and classification of LC events. Further, we highlighted the characteristic differences between ILC and ULC by comparing their duration, crash likelihood, and driver discomfort. The likelihood, duration of exposure, and severity of LC crashes were captured using the surrogate safety measures such as Anticipated Collision Time (ACT), Time-Exposed ACT (TE-ACT), and Time-Integrated ACT (TI-ACT). The driver's discomfort was measured using acceleration noise and the number of sign inversions in the acceleration profile. The Kruskal–Wallis–ANOVA test on the above attributes and the sensitivity analysis of the duration models confirm that ILC and ULC are characteristically different. © 2021 Hong Kong Society for Transportation Studies Limited.