A dynamic dispatching method in the unmanned airport baggage transportation system

Baggage transportation system with unmanned electric vehicles is one of the emerging research topic of the civil aviation airport. However, the battery capacity of electric delivery vehicles limits the distance and load capacity during driving, which has an influence on the decision of vehicles scheduling. To address the above problems, this paper propose a dynamic scheduling model of unmanned electric baggage transportation vehicles in airport containing both load capacity and battery capacity constraints. We also design a dynamic scheduling algorithm to process updating of flights massages in real time using the GCN-CNN-GRU neural network framework to determine the real-time driving condition of vehicles. The experiments under simulated scenarios have proved the performance of our method.