The pre-insertion process called anticipation is an essential component of a lane-changing manoeuvre. There is little empirical research regarding the impact of anticipation. Thus, this paper aims to explore the behaviour of the new follower (NF) in the target lane when it encounters anticipation by using new trajectory datasets. The changing magnitude of the reaction pattern is proposed to identify the NF’s behaviour. We find that the anticipation significantly affects the NF’s movement in terms of gap creation and speed reduction. Then, we conduct a detailed analysis of critical variables to reveal their relationship with the NF’s behaviour. Following this, we develop binary logistic models to predict the NF’s behaviour, resulting in a good performance. It also suggests that the NF’s behaviour is highly related to the anticipation-related variables. The transferability test results show that this model can be directly used in different locations and times with satisfactory accuracy.@en

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.@en