Cooperative Scheduling Method and Simulation of Autonomous Vehicle Platoons for Airport Curbside Area

To address the prevalent issues of traffic congestion and low operational efficiency at the Curbside Area of large hub airports, this paper proposes a cooperative scheduling strategy for vehicle platoons in an autonomous driving environment. The strategy establishes a functional model that includes a waiting area, a curbside service area, and an emergency lane. It features state-management-based algorithms for dynamic path allocation, waiting-waking, and cooperative docking to achieve fine-grained control over the entire process of platoon arrival and departure. To validate the strategy’s effectiveness, a case study of a major airport was conducted using a joint SUMO and Python simulation platform, with parameters calibrated from measured traffic data. The results demonstrate that the proposed strategy can reduce the average vehicle delay by 77.9% under peak traffic flow (1100 Veh/h). Furthermore, the system maintains high efficiency and stability even under oversaturated traffic conditions (1500 Veh/h). This research provides new theoretical and practical support for the intelligent management of airport landside traffic.