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Modeling driver lane-changing aggressiveness under target-lane interference: A Bayesian approach using naturalistic

Rui Shen¹, Lu Ma¹, Xuedong Yan²

¹School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Accident; Analysis and Prevention

|May 24, 2026

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Drivers adopt safer lane-changing strategies in bad weather, reducing maximum lateral velocity. Vehicle interactions, however, can increase aggressive maneuvers, impacting traffic safety.

Area of Science:

Traffic Safety
Automotive Engineering
Behavioral Science

Background:

Aggressive lane-changing behavior poses significant traffic safety risks due to sharp lateral movements and speed variations.
Maximum lateral velocity is a key metric for quantifying lane-change intensity and driver maneuver characteristics.

Purpose of the Study:

To analyze the characteristics of lane-changing events using real-world data.
To investigate the influence of environmental and driving conditions on lane-change intensity.
To identify key factors shaping driver lane-changing behavior.

Main Methods:

Extraction and descriptive statistical analysis of 1,646 real-world lane-changing events from CQSkyEyes trajectory data.
Development of a Bayesian model to assess variations in lane-change intensity.

Keywords:

Heterogeneity Lane-changing behavior Random parameters Traffic safety

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Analysis of environmental factors (e.g., weather) and surrounding vehicle interactions.

Main Results:

Complex weather conditions correlate with more conservative lane-changing strategies, indicated by reduced maximum lateral velocity.
Hazardous time-to-collision (TTC) levels further encourage conservative driving behavior.
The proximity of vehicles in the target lane increases the probability of aggressive lane-change maneuvers.

Conclusions:

Maximum lateral velocity serves as a reliable quantitative indicator of driver behavior during lane changes.
Findings provide insights for enhancing traffic safety management strategies.
Results are applicable to the design and development of autonomous driving systems.