Estimation about service time of flight ground support based on deep neural network

Abstract

In order to improve the efficiency and decision-making ability of airport operation support, the realization of estimation about service time of flight ground support can reduce the time and economic losses caused by flight delays. Considering the complexity and particularity of the service process, this article started from the analysis of the flight ground support process and constructed a mathematical model of the service time. The method of Principal Component Analysis (PCA) was used to reduce the correlation between variables, and a service time prediction model of flight ground support based on Deep Neural Network (DNN) was established. Finally, the flight support operation data of an airport were selected for simulation and verification. Experimental results show that the average absolute error of service time prediction can reach 2.709 min, the proposed model can effectively estimate the service time of flight support and has higher accuracy.