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Nowadays, the rapid growth of civil aviation transportation demand has led to more frequent flight delays. The major problem of flight delays is restricting the development of municipal airports. To further improve passenger satisfaction, and reduce economic losses caused by flight delays, environmental pollution and many other adverse consequences, three machine learning algorithms are constructed in current study: random forest (RF), gradient boosting decision tree (GBDT) and BP neural network (BPNN). The departure flight delay prediction model uses the actual data set of domestic flights in the United States to simulate and verify the performance and accuracy of the three models. This model combines the visual analysis system to show the density of departure flight delays between different airports. Firstly, the data set is reprocessed, and the main factors leading to flight delays are selected as sample attributes by principal component analysis.

Transporting baggage is critical in airport ground support services to ensure smooth flight operations. However, the scheduling of baggage transport vehicles faces challenges related to low efficiency and high costs. A multi-objective optimization vehicle scheduling model is proposed to address these issues, considering time and space costs, vehicle utilization, and passenger waiting time. An improved genetic algorithm (IGA) based on the large-scale neighborhood search algorithm is proposed to solve this model. The simulation experiment is conducted using actual flight data from an international airport. The IGA algorithm is compared with the standard genetic algorithm (SGA) based on experimental results, revealing that the former achieves convergence in a significantly shorter time. Moreover, the scheduling paths of baggage cars that violate flight service time window requirements are significantly lower in the final scheduling scheme under the IGA algorithm than in SGA.