Data-driven trajectory-based analysis and optimization of airport surface movement

Driven by the recent deployment of equipment and the availability of data, aircraft trajectories on the surface can be tracked continuously, and the detailed analysis of surface operations is enabled to measure airport operational performance and prepare for collaborative decision making. This paper proposes a hybrid approach combining traffic analysis and optimization to address the airport surface movement problem. Important metrics of surface management such as runway utilization, taxi routes, taxi times are extracted and characterized based on Automatic Dependent Surveillance-Broadcast (ADS-B) messages. After recognizing the congestion bottleneck, a trajectory-based optimization model is proposed, and an adapted simulated annealing heuristic is applied to solve the related problem. Two concepts for optimizing surface operations: arrival taxi reroute and departure controlled pushback are evaluated. The proposed approach is illustrated for Beijing Capital International airport. Computational experiments show that a mean taxi-in time reduction of 5.1 min is achieved for the rerouting arrivals, and the mean taxi-out time is reduced by 3.7 min with gate holding strategy. This data-driven approach allows advanced characterization of complex surface operations and enables collaborative decision assistance tools for managing surface movement efficiently.