Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2025

Abstract

Discretionary lane-changing behavior is one of the most common highway operations, which seriously affects traffic efficiency and safety. Nowadays, connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. As a result, a mixed traffic environment with traditional human-driven vehicles (HDVs) and CAVs will persist for the foreseeable future. To achieve effective automatic lane-changing maneuvers, it’s necessary to propose a lane-changing decision model for heterogeneous traffic flow on two-lane highways. This paper firstly extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using quintic polynomial curves to accommodate heterogeneous traffic flow, and introduces hyperbolic tangent transition models as well as modified virtual vehicle models to enable HDVs and CAVs to navigate the hybrid conditions of car-following and lane-changing, respectively. Then, this paper designs a hierarchical lane-changing gaming (HLCG) framework based on Stackelberg game theory and Harsanyi transformation theory, including a CAV decision model integrating dynamic safety domains and optimal lane-changing trajectories, and an HDV decision model considering different human driving styles, with a comprehensive payoff function concerning collision safety, traffic efficiency, and ride comfort. Finally, to verify the overall performance of the proposed HLCG decision model, simulations under different traffic densities and CAV penetration rates are carried out. The results show that the cooperation between CAVs can improve collision safety, stability and ride comfort during lane changes. Moreover, the proposed HLCG framework outperforms traditional game theory in terms of traffic efficiency, safety, ride comfort and shockwave generation across different traffic conditions.

Keywords

operations, automated/autonomous/connected vehicles, car-following, lane changing, microscopic traffic models

Discipline

Artificial Intelligence and Robotics | Transportation

Research Areas

Integrative Research Areas

Publication

Transportation Research Record

Volume

2679

Issue

10

First Page

449

Last Page

469

ISSN

0361-1981

Identifier

10.1177/03611981251342246

Publisher

SAGE Publications

Additional URL

https://doi.org/10.1177/03611981251342246

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