When a traffic crash occurs, following vehicles need to change lanes to bypass the obstruction. We define these maneuvers as post-crash lane changes (LCs). In such scenarios, vehicles in the target lane may refuse to yield even after the lane change has already begun, increasing the complexity and crash risk of post-crash LCs. However, the behavioral characteristics and motion patterns of post-crash LCs remain unknown. To address this gap, we construct a post-crash LC dataset by extracting vehicle trajectories from drone videos captured after crashes. Our empirical analysis reveals that, compared to mandatory LCs (MLCs) and discretionary LCs (DLCs), post-crash LCs exhibit longer durations, lower insertion speeds, and higher crash risks. Notably, 79.4% of post-crash LCs involve at least one instance of non-yielding behavior from the new follower, compared to 21.7% for DLCs and 28.6% for MLCs. Building on these findings, we develop a novel trajectory prediction framework for post-crash LCs. At its core is a graph-based attention module that explicitly models yielding behavior as an auxiliary interaction-aware task. This module is designed to guide both a conditional variational autoencoder and a Transformer-based decoder to predict the lane changer's trajectory. By incorporating the interaction-aware module, our model outperforms existing baselines in trajectory prediction performance by more than 10% in both average displacement error and final displacement error across different prediction horizons. Moreover, our model provides more reliable crash risk analysis by reducing false crash rates and improving conflict prediction accuracy. Finally, we validate the model's transferability using additional post-crash LC datasets collected from different sites.

Lane-changing (LC) in congested traffic has been identified as a trigger for the sudden deceleration behavior of the new follower in the target lane, leading to severe traffic disturbances. Thus, investigating the response of the new follower to an LC maneuver is an important research topic in the literature. To date, numerous efforts have been devoted to understanding the impact of the lane changer on the new follower after the insertion, while less attention has been given to this influence during the pre-insertion stage (anticipation). Therefore, this paper aims to establish a new car-following (CF) model to capture the new follower's driving behavior during anticipation. Specifically, we introduce an attention mechanism deviating from Newell's CF rules to quantify the impact of anticipation. Then, we apply a neural network with an attention layer to estimate the attention mechanism and incorporate it into the Newell CF model, which yields a new CF model, denoted as CF_Attention. Using real-world trajectory data, we design three experiments and select three representative CF models to validate the CF_Attention. The results indicate that the CF_Attention outperforms the other models in predicting the new follower's trajectory, which is not affected by the heterogeneous behavior of the new follower and the anticipation duration. Additionally, the CF_Attention is proven effective in capturing the speed-space relationship and the formation of oscillation. Finally, our transferability test suggests that the CF_Attention is promising for different locations and times without requiring retraining. The results of this study could advance the integration of the LC impact and CF behavior, and could be implemented into commercial traffic simulation programs to describe vehicle movements in traffic flow more accurately.