Abstract: Flight ground service time prediction is one of the key issues in improving the airport operational efficiency and decision making capacity. Taking into account the complexity, particularity and uncertainty of the service process, a Gaussian probability model of flight ground service resource in place time is established, a flight ground service time prediction model based on the deep neural network is proposed. According to the regular changes of operational data, the model parameters are adjusted to reducet the generalization error caused by other factors. The research results show that the average absolute error of time prediction under single flight is 4.479 min less than that of the multiple flights, the model evaluation score reaches 94.608, and the prediction accuracy is 3%~5% higher than that of the traditional BP neural network and Bayesian network method.

Key words: air transportation, flight ground service, time prediction, deep neural network, Gaussian probability model