基于核主成分分析的空域复杂度无监督评估

空域复杂度评估作为衡量空域运行态势、管制员工作压力的关键手段，是运行调控的基础。由于影响因素众多，不同因素间耦合关联复杂，且标定样本很难获取，空域复杂度的准确评估被公认为航空领域的一个挑战性问题。提出了一种空域复杂度的无监督评估方法。首先通过核主成分分析挖掘原始样本各维度的非线性耦合关系，准确提取能够最大化复杂度评估信息量的主成分，进一步设计了可按需定制的主成分聚类方法，实现了无监督条件下空域复杂度的准确评估，为空域划分、流量管理提供了有效的技术支撑。     

基于灰色关联聚类的空中交通复杂度评估方法

为了准确评价空中交通态势的复杂程度,进行有效的空中交通管理,提出了一种空中交通复杂度评估方法。在已有的理论研究成果及实际运行经验的基础上,归纳了定义明确、可定量分析的基础空中交通指标集,使用灰色关联聚类方法对指标集进行聚类分析,利用信息熵理论对聚类指标求解权重,加权求和获得空中交通复杂度。方法弥补了目前使用单一维度指标描述空中交通运行态势的弊端,能更好地体现空中交通复杂度。     

Multiple air route crossing waypoints optimization via artificial potential field method

Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization, capacity and resilience in dealing with air traffic congestion and delay. However, research is lacking on the optimization of multiple Crossing Waypoints (CWPs) in the fragmented airspace separated by Prohibited, Restricted and Dangerous areas (PRDs). To tackle this issue, this paper proposes an Artificial Potential Field (APF) model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles. An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs, air route segments and PRDs. Based on the framework, an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost. The proposed model is applied to a busy controlled airspace. And the obtained results show that after optimization the safety-related indicators: conflict number and controller workload reduced by 7.75% and 6.51% respectively. As for the cost-effectiveness indicators: total route length, total air route cost and non-linear coefficient, declined by 1.74%, 3.13% and 1.70% respectively. While the predictability indicator, total flight delay, saw a notable reduction by 7.96%. The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.     

Comparison of alternative route selection strategies based on simulation optimization

Air traffic flow management (ATFM) is a collaborative process between the airspace provider and the airspace users. The result of the collaboration should be an outcome that maxi-mizes the utility of the system without excessively penalizing any of the agents. This paper develops a discrete-event simulation model which consists of aggregate departure/arrival airports, flight routes, and sectors for evaluating the alternative collaborative route selection strategy. Given the different perspectives from air traffic control center (ACC) and airlines, eight performance-metrics and five alternative route selection strategies represent the past, current and proposed air traffic flow management operations that were evaluated. The Monte Carlo method combined with the Optimal Computing Budget Allocation (OCBA) simulation optimization technique is employed to assess the performance of different strategies. A case study of the upper air routes in central and southern China shows that the proposed model can be readily implemented to simulate different kinds of air traffic flow management strategies and predict the effect of changes on the airspace sys-tem. It also shows that the proposed alternative collaborative route selection strategy is an effective way in alleviating the en-route traffic congestion.     

一种终端区空中交通复杂度的计算方法

将终端区的空中交通复杂度分为静态复杂度和动态复杂度,提出了终端区空中交通复杂度的一种计算方法。通过考虑航空器数量、机型混杂比例和进离场航迹相互影响关系等因素建立空中交通复杂度模型。有效避免了因管制员工作水平差异等人为因素带来的不确定性影响,能更加客观地反应终端区的空中交通复杂度,具有通用性。最后通过Matlab实例仿真,验证了方法的有效性和实用性。     

不同管制方法下空域运行的燃油经济性与仿真分析

为了提高空域运行的燃油经济性,提出了不同管制方案下空域运行的燃油经济性模型,通过仿真实验．对比分析了不同管制方案和飞机群体在空域运行的燃油消耗量。实验结果表明：通过使用该模型可以从管制方法上节省整体空域中飞机群体的燃油消耗,同时证明了该模型的可行性。指出了不同管制方法对空域运行的燃油经济性的影响和空管绩效评估的一项新指标。     

基于管制员工作负荷的终端区容量评估

对现有的基于管制员工作负荷的空域容量评估方法进行分析,并比较各自的特点。提出了改进的基于管制员工作负荷的容量评估模型,模型的建立不仅考虑了管制员工作负荷的不同类别,即通信负荷、非通信负荷和思考负荷,还考虑了空域内航路结构的复杂性,即区分空域内不同航路走向上运行的航空器数量。描述了基于新建模型的容量评估方法。以哈尔滨机场终端区为背景,在雷达管制的基础上,使用新建立的模型,采用回归分析方法,计算了终端区容量,验证了所提模型和方法的可行性与适用性。     

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic con gestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas (PRDs).In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors (NSLF) are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in mainland China is collected as the origin-destination (OD) air port pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67％,while the total length of flight segments and air routes drop by 32.03％ and 5.82％ respectively.The NSLF decreases by 5.82％ with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22％.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.     

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by reasonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimization problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm(MPEA) framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is proposed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution(CC) algorithm combined with non-dominated sorting genetic algorithm II(NSGA-II) is introduced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multiobjective genetic algorithm(MOGA), multi-objective evolutionary algorithm based on decomposition(MOEA/D), CC-based multi-objective algorithm(CCMA) as well as other two MPEAs with different migration interval strategies.     

地面管制阶段航空器等待策略研究

地面等待策略是空中交通流量管理的主要组成部分,有利于节约飞行成本,提高安全水平。建立了空中交通多目标地面等待策略的整数线性规划模型,模型包括所有航班的总等待时间最短和总等待费用最小两个优化指标,约束条件考虑了指定空域容量的限制和各航班等待时段上限的限制。考虑到该优化问题求解的计算复杂度,应用基于自然法则的模拟退火随机搜索优化算法来解决该问题。数值模拟算例表明了所建立数学模型的正确性和算法的可行性和有效性。