A Road-Level Transport Network Model with Microscopic Operational Features for Aircraft Taxi-Out Time Prediction

For aircraft departure, which is a process of multi-resource coordination, strict time limitations, and complex condition constraints, the optimization of taxi-out time prediction is critical for enhancing airport surface operational efficiency, optimizing runway slot utilization, and reducing aircraft ground delay and fuel consumption. By combining aircraft taxi path and network traffic flow features, a refined airport road-level transport network model is constructed to accurately characterize the taxi path topology and node-edge attributes. On this basis, two new micro-features are introduced: estimated taxi time and the number of handovers. Experimental results show that after the introduction of the micro-features, the prediction accuracy of the taxi-out time prediction model within the error of 1 min increases from 49.29% to 54.41%, and the prediction accuracy within the error of 5 min reaches 99.42%. This method effectively addresses the limitations of traditional models that focus solely on the overall taxiing process while neglecting microscopic airfield network dynamics and time consumption during control handover procedures. The method can be integrated into the Airport Collaborative Decision Making (A-CDM) system to provide minute-level support for departure taxi-out time prediction, thereby providing a more precise and operationally aligned temporal benchmark for intelligent apron operations scheduling, aircraft sequencing optimization, and other collaborative decision making processes.