Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories

Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories

Chen, K. (Southeast University)
Li, Zhibin (Southeast University)
Liu, Pan (Southeast University)
Knoop, V.L. (TU Delft Transport and Planning)
Han, Yu (Southeast University)
Jiao, Y. (TU Delft Transport and Planning)

2024

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.

Flow efficiency
Forced lane-changing behavior
Lane-changing impact
Traffic safety
Trajectory data analysis

http://resolver.tudelft.nl/uuid:133b675e-bf92-4d7e-bae3-c0837d7f0039

https://doi.org/10.1016/j.aap.2024.107622

2024-11-08

0001-4575

Accident Analysis & Prevention, 203

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

© 2024 K. Chen, Zhibin Li, Pan Liu, V.L. Knoop, Yu Han, Y. Jiao