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

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

Chen, Kequan (Southeast University)
Knoop, V.L. (TU Delft Transport and Planning)
Liu, Pan (Southeast University)
Li, Zhibin (Southeast University)
Wang, Yuxuan (Southeast University)

2023

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.

Anticipation
Car-following model
Lane-changing impact
Trajectory data

http://resolver.tudelft.nl/uuid:5b9a2c92-6e2f-4da6-9a7f-a598ef190c6a

https://doi.org/10.1016/j.trc.2023.104110

2023-09-30

0968-090X

Transportation Research. Part C: Emerging Technologies, 150

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.

Institutional Repository

journal article

© 2023 Kequan Chen, V.L. Knoop, Pan Liu, Zhibin Li, Yuxuan Wang