Modeling the impact of lane-changing’s anticipation on car-following behavior

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, the authors introduce an attention mechanism deviating from Newell’s CF rules to quantify the impact of anticipation. Then, they 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, the authors 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, the authors' 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.


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  • Accession Number: 01883971
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 31 2023 8:03AM