基于多机场终端区交通态势的航班延误预测

为了针对性地制定后续优化措施，以降低多机场终端区内航班延误所带来的不利影响，并提高多机场系统内各机场的运营效率，进行多机场终端区航班延误的预测研究。首先，考虑多机场终端区交通态势对航班延误的影响，在对多机场终端区交通态势进行分析的基础上，建立了6个描述终端区交通态势的指标。接着，构建反向传播（back propagation，BP）神经网络航班延误预测模型，将终端区交通态势指标、航班信息和天气环境数据等作为输入，航班延误时间作为输出，并利用粒子群优化算法（particle swarm optimization，PSO）优化BP神经网络进行训练。通过实例验证和分析，基于多机场终端区交通态势的航班延误预测能够有效提高预测准确率，同时，通过粒子群优化BP神经网络的预测模型预测准确率均高于一般的考虑交通态势的BP和遗传算法优化的BP神经网络模型（genetic algorithm and back propagation,GA-BP）。

Flight delay prediction based on traffic dynamics in multi-airport terminal areas

In order to target subsequent optimisation measures to reduce the negative impact of flight delays in multi-airport terminal areas and to improve the operational efficiency of each airport in a multi-airport system, this paper conducts a study on the prediction of flight delays in multi-airport terminal areas. Firstly, the impact of multi-airport terminal area traffic dynamics on flight delays is considered. Based on the analysis of multi-airport terminal area traffic dynamics, six indicators describing terminal area traffic dynamics are established. Then, a BP (back propagation) neural network flight delay prediction model was constructed, taking the terminal area traffic situation indicators, flight information and weather environment data as input and flight delay time as output, and the BP neural network was optimized using particle swarm optimization algorithm (PSO) for training. The results show that flight delay prediction based on multi-airport terminal area traffic situation can effectively improve the prediction accuracy, and the prediction accuracy of the prediction model by the particle swarm optimized BP neural network is higher than that of the general BP and GA-BP (genetic algorithm and back propagation) models considering traffic situation.