中文摘要：

基于空中交通复杂程度刻画管制工作负荷是当前空中交通管理领域的研究热点.本文采集了厦门空管站的雷达数据,计算得出10个空中交通复杂性评价指标数值,通过共线性诊断发现复杂性指标间存在较强的多重共线性.在利用岭迹图对复杂性评价指标进行筛选的基础上,建立岭回归—BP神经网络组合模型对管制员工作负荷进行预测,并通过实测陆空通话数据进行验证.结果表明,本文提出的岭回归—BP神经网络组合模型收敛速度快、训练时间少;组合模型的均方误差、均方根误差、平均绝对误差、平均绝对相对误差等4项性能指标都相对较小,预测精度较高.

英文摘要：

It is becoming a new hot topic in the field of air traffic management that evaluating the controller＇s workload by the traffic complexity factors. Based on the radar data of Xiamen air traffic control station, 10 typical complexity evaluation factors were calculated. The strong multi-co-linearity among various complexity factors is discovered through co-linearity diagnosis. Using the ridge trace plot of ridge regression, the complexity evaluation factors are selected, and the combined model of ridge regression and neural network are established to predict the controller＇s workload. The forecasting results are verified by the pilot/controller voice communication data. It shows that the combination model of ridge regression and BP neural network has fast convergence speed and less training time. The combined forecasting model has high precision because four performance indexes such as mean square error, root mean square error, mean absolute error and mean absolute relative errors are relatively small.