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

Kequan Chen, Victor L. Knoop, Pan Liu*, Zhibin Li, Yuxuan Wang

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
54 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number104110
Number of pages21
JournalTransportation Research Part C: Emerging Technologies
Volume150
DOIs
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Anticipation
  • Car-following model
  • Lane-changing impact
  • Trajectory data

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