全国民航空中交通量长期预测技术研究

全国民航空中交通量的长期预测是空中交通管理部门制定空域规划方案的重要依据.本文基于GM（1,1）模型和最小二乘法原理,首次提出了空中交通流量灰组合长期预测模型;同时根据我国民航1985~2008年的飞机起降架次的历史数据,研究了时间序列预测、回归预测以及神经网络预测三种常用预测方法对中国民航空中交通量进行预测的适应性;通过分析我国民航空中交通量数据的特点以及各种预测模型的预测结果表明,本文提出的灰组合预测模型在上述各种预测模型中预测精度最高.

FLIGHT CONFLICT DETECTION AND RESOLUTION

The air traffic control (ATC) systems are facing more and more serious congestive because of the increasing of air traffic flow in China. One of the most available ways to solve the problem is 'free flight' that the pilots may choose the air route and flight speed suitable for them. But this will lead to the difficulties for the controllers. This paper presents how ATC genetic algorithms can be used to detect and to solve air traffic control conflicts in free flight. And it also shows that this algorithm perfectly suits for solving flight conflicts resolution because of its short computing time.

终端区进场流的路径选择研究

在实际运行中,通常依据管制员的经验对进场航班流的路径进行管理,缺乏科学性。针对终端区进场流的路径选择与进场排序的流量管理问题,以进场航班的总完成时间最短为目标,建立基于时间窗与位置约束的路径选择与排序的数学模型。采用先选择路径再排序的循环寻优的方法进行求解,将禁忌搜索(TS)与排序算法相结合,设计了TS-FCFS与TS-DP两种算法,着重建立了基于关键路径的邻域结构,并加入有效的重置(RESTAR)策略,使算法快速有效的收敛至最优解。最后借助SIMMOD仿真平台验证算法的可行性。以北京首都机场21架航班为例,仿真结果显示,TS-FCFS与TS-DP两种方法较原计划的进场路径总完成时间分别节约了149s与175s。该路径选择算法降低了进场时间,提高了终端区进场效率。通过合理优化进场航班流的路径选择与进场排序,平衡了跑道负荷,减少了航空器间潜在的冲突,同时为管制员调配进场航班流提供了合理有效的路径选择建议。

Terminal Traffic Flow Prediction Method Under Convective Weather Using Deep Learning Approaches

In order to improve the accuracy and stability of terminal traffic flow prediction in convective weather,a multi-input deep learning(MICL)model is proposed.On the basis of previous studies,this paper expands the set of weather characteristics affecting the traffic flow in the terminal area,including weather forecast data and Meteorological Report of Aerodrome Conditions(METAR)data.The terminal airspace is divided into smaller areas based on function and the weather severity index(WSI)characteristics extracted from weather forecast data are established to better quantify the impact of weather.MICL model preserves the advantages of the convolution neural network(CNN)and the long short-term memory(LSTM)model,and adopts two channels to input WSI and METAR information,respectively,which can fully reflect the temporal and spatial distribution characteristics of weather in the terminal area.Multi-scene experiments are designed based on the real historical data of Guangzhou Terminal Area operating in typical convective weather.The results show that the MICL model has excellent performance in mean squared error(MSE),root MSE(RMSE),mean absolute error(MAE)and other performance indicators compared with the existing machine learning models or deep learning models,such as Knearest neighbor(KNN),support vector regression(SVR),CNN and LSTM.In the forecast period ranging from 30 min to 6 h,the MICL model has the best prediction accuracy and stability.

Identification of Similar Air Traffic Scenes with Active Metric Learning

The rapid growth of air traffic has continuously increased the workload of controllers,which has become an important factor restricting sector capacity.If similar traffic scenes can be identified,the historical decision-making experience may be used to help controllers decide control strategies quickly.Considering that there are many traffic scenes and it is hard to label them all,in this paper,we propose an active SVM metric learning(ASVM2L)algorithm to measure and identify the similar traffic scenes.First of all,we obtain some traffic scene samples correctly labeled by experienced air traffic controllers.We design an active sampling strategy based on voting difference to choose the most valuable unlabeled samples and label them.Then the metric matrix of all the labeled samples is learned and used to complete the classification of traffic scenes.We verify the effectiveness of ASVM2L on standard data sets,and then use it to measure and classify the traffic scenes on the historical air traffic data set of the Central South Sector of China.The experimental results show that,compared with other existing methods,the proposed method can use the information of traffic scene samples more thoroughly and achieve better classification performance under limited labeled samples.

Forecast of Air Traffic Controller Demand Based on SVR and Parameter Optimization

As the main body of air traffic control safety,the air traffic controller is an important part of the whole air traffic control system. According to the relevant data of civil aviation over the years,a mapping model between flight support sorties and air traffic controller demand is constructed by using the prediction algorithm of support vector regression(SVR) based on grid search and cross-validation. Then the model predicts the demand for air traffic controllers in seven regions. Additionally,according to the employment data of civil aviation universities,the future training scale of air traffic controller is predicted. The forecast results show that the average relative error of the number of controllers predicted by the algorithm is 1.73%,and the prediction accuracy is higher than traditional regression algorithms. Under the influence of the epidemic,the demand for air traffic controllers will decrease in the short term,but with the control of the epidemic,the demand of air traffic controllers will return to the pre-epidemic level and gradually increase. It is expected that the controller increment will be about 816 by 2028. The forecast results of the demand for air traffic controllers provide a theoretical basis for the introduction and training of medium and long-term air traffic controllers,and also provide method guidance and decision support for the establishment of professional reserve and dynamic control mechanism in the air traffic control system.

Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty

This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.