基于不确定需求预测的概率空域拥挤管理方法研究
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摘要
随着航空运输业的飞速发展，空域系统运行压力日益增大、运行环境多变复杂，不确定性因素对空中交通态势的影响日益突出。从不确定性角度缓解空域运行压力，逐渐成为欧美等航空发达国家近年来关注的热点之一，也是精细化、科学化研究我国空域拥挤问题的重要课题。本论文在深入分析交通需求随机性变化机理的同时，从风险管理的角度定位空域拥挤管理，探讨传统确定性的空域拥挤管理方式向不确定性方式的转变过程，有助于进一步完善空中交通需求预测方法，改进空域拥挤管理理论，提高航空运输服务的经济效益和社会效益。
本论文在借鉴国外先进研究成果的基础上，结合我国空域拥挤管理实际问题，利用空中交通流量管理、流量统计与预测、多目标优化、空域拥挤风险管理等技术，从不确定交通需求预测出发，将风险预测、风险解决和风险管理决策融入空域拥挤缓解过程中，通过整合空中交通流量的预测与调配过程，初步建立了一套较为完整的概率空域拥挤管理理论方法。主要研究内容包括：
（1）系统论述了概率空域拥挤管理研究基础。首先，定义了概率空域拥挤管理的基本概念要素，包括空中交通管理、空中交通流量管理、风险管理、概率交通需求、不确定空域容量、空域拥挤风险和空域拥挤风险管理等，论述了各概念间的衍生推演关系，明确了概率空域拥挤管理概念要素体系。然后，分析了概率空域拥挤管理的基本原理，阐明了实施概率空域拥挤管理的具体流程。最后，介绍了所采用的空中交通流量管理、风险管理、空中交通流量统计与预测、空域容量评估、多目标优化，以及空域拥挤风险管理等主要技术。
（2）研究了空域拥挤风险预测方法。首先，提取了影响扇区交通需求预测的主要因素，通过分析航空器的到达扇区时间、扇区飞行时间和离开扇区时间三因素的随机特征，建立了空域扇区概率需求预测模型。然后，将空域扇区概率需求预测模型先后与确定性和不确定性的空域容量相结合，建立了空域拥挤风险预测模型及方法。最后，通过算例仿真表明，所建模型和方法可以量化扇区交通需求的不确定性分布及变化机理，分析不确定性因素对空中交通需求与空域容量的影响，并掌握二者的变化与匹配，从而确定未来一段时间内空域范围中可能发生的空域拥挤风险问题。
（3）研究了基于局部优化的空域拥挤风险解决方法。首先，将空域拥挤风险预测模型引入空域拥挤风险解决机制，完成了空域拥挤风险预测模型在风险解决过程中的转化；通过预测高风险拥挤空域及时段，以空域最高拥挤风险为对象，从平衡空域拥挤的运行成本与运行风险出发，建立了基于局部优化的空域拥挤风险解决模型和方法，综合处理空域内发生拥挤后续可能性、全体航班的总延误时间、不同空域用户延误分配公平性以及解决策略的影响等多个目标。然后，利用高维多目标优化的NSGA2改进算法，实现了高拥挤风险解决策略的初次优化。最后，通过算例仿真结果表明，所建模型及算法不仅能在合理的时间内为空域内航空器找到较优离场时间和较优飞行路径，还能降低空域拥挤后续发生的可能性和全体航空器的运行成本，提高空域用户延误分配的公平性，降低对原有航空器飞行计划的影响程度，缓解空域扇区的最高拥挤风险。
（4）研究了基于全局优化的空域拥挤风险解决方法。首先，针对初次优化后尚未解决的空中交通流全局优化问题，综合考虑从交通需求分布、平衡以及管制员负荷等问题，建立了基于全局优化的空域拥挤风险解决模型，并利用多目标遗传算法，实现了空域拥挤风险解决策略的二次优化。结合空域拥挤风险解决策略，选取了符合实际运行需要的风险损失评价指标，建立了空域拥挤风险评价模型，并利用空域拥挤风险管理决策方法，确定了空域拥挤风险解决策略的实施时间和空域拥挤风险概率阈值。最后，通过算例仿真结果表明，所建模型和方法可以从空域运行的全局出发，从整体上进一步解决了交通流全局分布及各扇区的运行负荷等问题，有助于平衡与协调交通流运行全局，还可以获得较为适宜的空域拥挤风险解决策略实施时间和空域拥挤风险概率阈值，从而实现空域拥挤风险管理过程的进一步细化。
最后，论文总结了基于不确定需求预测的概率空域拥挤管理方法研究成果，指出本文在不确定交通需求预测方面可以向更加细致的微观交通流层面拓展，在空域拥挤风险管理过程中可以更加深入地探讨人为因素的影响，在理论研究的基础上还需向实际应用系统转化，并展望了今后的研究方向。
With the rapid development of air transportation industry, airspace system performance presstureis increasing, and the airspace environment has been more and more complicated. Because uncertainfactors have increasingly been affacting the air traffic, how to releivate the airspace system pressturefrom uncertainty theory perspective is becoming a focus in the field of air traffic management amongsome advanced aviation counties. In order sophisticat the process of airspace congestion problem inChina, based on the current situation of airspace system, we deeply analyzed the random changemechamism of air traffic demand. Meanwhile, we investigate the airspace congestion managementfrom the point of risk management, and discussed how the traditional determinated airspacecongestion management gradually transferred to the uncertain management. The research is help toconsummate the method of air traffic demand prediction, to improve the theory of airtspacecongestion management, and promote the economic and social benifit
In this paper, based on some abroad sophisticated experiences, we researched the praticalairspace congestion problem. We utilized the thecnologies in the field of air traffic flow management,air traffic flow stasatics and prediction, multi-objective optimization and airspace congestion riskmanagement, and discussed the process of uncertain air traffic demand prediction, and interegratedthe risk prediction, resolution and decision into the airspace congestion relievation. The main contentof the paper is as follows:
(1) Reaserach base of airspace congestion risk management was introduced. We defined the basicconcepts including air traffic management, air traffic flow management, risk management, risk trafficprediction, uncertain airspace capacity, airspace congestion risk and airspace congestion riskmanagement, and also established the deviation relationship between these concepts. Then, weanalyzed the basic theory of airspace congestion management, and provided the probabilistic airspacecongestion management definition, and its specific process and inherent. Moreover, based on theconcepts of probabilisitic airspace congestion management, we developed the relationship betweenthe concepts and the technologies, and introduced the technologies including air traffic flowmanagement, risk management, air traffic flow statictics and prediction, airspace capacity evalution,multi-objective optimization, and airspace congestion risk management.
(2) Methodology of airspace congestion risk prediction was studied. First, we exacted the mainfactors influenceing the airspace sector’s traffic demand prediction, and established the airspace sectorprobabilistic demand prediction model through analyzing the random features of the aircraft sector entry time, sector exit time and existing time in the airspace sectors. Based on the model, the airtraffic demand probabilistic distribution was obtained, and the prediction errors could be quantified.Then, based on the airspace sector probabilistic traffic demand prediction model, we combined certainand uncertain airspace capacity ecaluation respectively, and established the airspace congestion riskprediction model and method. The simulation results show that the model and method we establishedcan quantify the traffic demand prediction uncertainty and its changing rules, and the influences of theuncertain factors on the traffic demands and capacities can be found out.
(3) Methodology of airspace congestion risk resolution based on partial optimization wasinvestigated. Based on the airspace congestion risk prediction, we established the airspace congestionresolution model. Through this model, we could comprehensively deal with several objectives,including the follow-up congestion, the total flight delay time, the different airspace users’ allocationequity and influence of the resolution strategies. Meanwhile, we designed high-dimensionalmultiobjective genetic NSGA2, realizing the initial optimization of the airspace congestion resolutionstrategies. The simulation results show that the model and algorithm established cannot only find outoptimum departure time and routes, but also reduce the risk of follow-up congestion, the operationcost and the influence, meanwhile, increase the equity of the airspace users.
(4) Methodology of airspace congestion risk resolution based on overal optimization wasresearched. First, in order to deal with the remaining problem of airspace congestion globaloptimization, we established the airspace congestion risk decision model, considering the distributionand balance of the air traffic flow, the workload of the air traffic controlors. Meanwhile, usingmulti-objective genetic algorithm, we reoptimized the airspace congestion risk resolution strategies.Combined with the airspace congestion resolution, we developed the airspace congestion riskevulation model, and utilized the decision tree to resolve the operating time of the airspace congestionresolution strategies and the threshold of the airspace congestion probability. Finally, the simulationresults show that the model and method established can deal with the air traffic global distribution andthe operation workload in each sector, and can obtain the suitable operating time and the airspacecongestion risk threshold.
Finally, in the thesis, we concluded the archievements of the probabilistic airspace congestionmanagement based on the uncertain traffic demand prediction, and pointed out the deficiency of thisresearch about microscopic traffic flow, human factors and performance system relization. Accordingto the conlusion above, we suggested the future study direction.
本论文在借鉴国外先进研究成果的基础上，结合我国空域拥挤管理实际问题，利用空中交通流量管理、流量统计与预测、多目标优化、空域拥挤风险管理等技术，从不确定交通需求预测出发，将风险预测、风险解决和风险管理决策融入空域拥挤缓解过程中，通过整合空中交通流量的预测与调配过程，初步建立了一套较为完整的概率空域拥挤管理理论方法。主要研究内容包括：
（1）系统论述了概率空域拥挤管理研究基础。首先，定义了概率空域拥挤管理的基本概念要素，包括空中交通管理、空中交通流量管理、风险管理、概率交通需求、不确定空域容量、空域拥挤风险和空域拥挤风险管理等，论述了各概念间的衍生推演关系，明确了概率空域拥挤管理概念要素体系。然后，分析了概率空域拥挤管理的基本原理，阐明了实施概率空域拥挤管理的具体流程。最后，介绍了所采用的空中交通流量管理、风险管理、空中交通流量统计与预测、空域容量评估、多目标优化，以及空域拥挤风险管理等主要技术。
（2）研究了空域拥挤风险预测方法。首先，提取了影响扇区交通需求预测的主要因素，通过分析航空器的到达扇区时间、扇区飞行时间和离开扇区时间三因素的随机特征，建立了空域扇区概率需求预测模型。然后，将空域扇区概率需求预测模型先后与确定性和不确定性的空域容量相结合，建立了空域拥挤风险预测模型及方法。最后，通过算例仿真表明，所建模型和方法可以量化扇区交通需求的不确定性分布及变化机理，分析不确定性因素对空中交通需求与空域容量的影响，并掌握二者的变化与匹配，从而确定未来一段时间内空域范围中可能发生的空域拥挤风险问题。
（3）研究了基于局部优化的空域拥挤风险解决方法。首先，将空域拥挤风险预测模型引入空域拥挤风险解决机制，完成了空域拥挤风险预测模型在风险解决过程中的转化；通过预测高风险拥挤空域及时段，以空域最高拥挤风险为对象，从平衡空域拥挤的运行成本与运行风险出发，建立了基于局部优化的空域拥挤风险解决模型和方法，综合处理空域内发生拥挤后续可能性、全体航班的总延误时间、不同空域用户延误分配公平性以及解决策略的影响等多个目标。然后，利用高维多目标优化的NSGA2改进算法，实现了高拥挤风险解决策略的初次优化。最后，通过算例仿真结果表明，所建模型及算法不仅能在合理的时间内为空域内航空器找到较优离场时间和较优飞行路径，还能降低空域拥挤后续发生的可能性和全体航空器的运行成本，提高空域用户延误分配的公平性，降低对原有航空器飞行计划的影响程度，缓解空域扇区的最高拥挤风险。
（4）研究了基于全局优化的空域拥挤风险解决方法。首先，针对初次优化后尚未解决的空中交通流全局优化问题，综合考虑从交通需求分布、平衡以及管制员负荷等问题，建立了基于全局优化的空域拥挤风险解决模型，并利用多目标遗传算法，实现了空域拥挤风险解决策略的二次优化。结合空域拥挤风险解决策略，选取了符合实际运行需要的风险损失评价指标，建立了空域拥挤风险评价模型，并利用空域拥挤风险管理决策方法，确定了空域拥挤风险解决策略的实施时间和空域拥挤风险概率阈值。最后，通过算例仿真结果表明，所建模型和方法可以从空域运行的全局出发，从整体上进一步解决了交通流全局分布及各扇区的运行负荷等问题，有助于平衡与协调交通流运行全局，还可以获得较为适宜的空域拥挤风险解决策略实施时间和空域拥挤风险概率阈值，从而实现空域拥挤风险管理过程的进一步细化。
最后，论文总结了基于不确定需求预测的概率空域拥挤管理方法研究成果，指出本文在不确定交通需求预测方面可以向更加细致的微观交通流层面拓展，在空域拥挤风险管理过程中可以更加深入地探讨人为因素的影响，在理论研究的基础上还需向实际应用系统转化，并展望了今后的研究方向。
With the rapid development of air transportation industry, airspace system performance presstureis increasing, and the airspace environment has been more and more complicated. Because uncertainfactors have increasingly been affacting the air traffic, how to releivate the airspace system pressturefrom uncertainty theory perspective is becoming a focus in the field of air traffic management amongsome advanced aviation counties. In order sophisticat the process of airspace congestion problem inChina, based on the current situation of airspace system, we deeply analyzed the random changemechamism of air traffic demand. Meanwhile, we investigate the airspace congestion managementfrom the point of risk management, and discussed how the traditional determinated airspacecongestion management gradually transferred to the uncertain management. The research is help toconsummate the method of air traffic demand prediction, to improve the theory of airtspacecongestion management, and promote the economic and social benifit
In this paper, based on some abroad sophisticated experiences, we researched the praticalairspace congestion problem. We utilized the thecnologies in the field of air traffic flow management,air traffic flow stasatics and prediction, multi-objective optimization and airspace congestion riskmanagement, and discussed the process of uncertain air traffic demand prediction, and interegratedthe risk prediction, resolution and decision into the airspace congestion relievation. The main contentof the paper is as follows:
(1) Reaserach base of airspace congestion risk management was introduced. We defined the basicconcepts including air traffic management, air traffic flow management, risk management, risk trafficprediction, uncertain airspace capacity, airspace congestion risk and airspace congestion riskmanagement, and also established the deviation relationship between these concepts. Then, weanalyzed the basic theory of airspace congestion management, and provided the probabilistic airspacecongestion management definition, and its specific process and inherent. Moreover, based on theconcepts of probabilisitic airspace congestion management, we developed the relationship betweenthe concepts and the technologies, and introduced the technologies including air traffic flowmanagement, risk management, air traffic flow statictics and prediction, airspace capacity evalution,multi-objective optimization, and airspace congestion risk management.
(2) Methodology of airspace congestion risk prediction was studied. First, we exacted the mainfactors influenceing the airspace sector’s traffic demand prediction, and established the airspace sectorprobabilistic demand prediction model through analyzing the random features of the aircraft sector entry time, sector exit time and existing time in the airspace sectors. Based on the model, the airtraffic demand probabilistic distribution was obtained, and the prediction errors could be quantified.Then, based on the airspace sector probabilistic traffic demand prediction model, we combined certainand uncertain airspace capacity ecaluation respectively, and established the airspace congestion riskprediction model and method. The simulation results show that the model and method we establishedcan quantify the traffic demand prediction uncertainty and its changing rules, and the influences of theuncertain factors on the traffic demands and capacities can be found out.
(3) Methodology of airspace congestion risk resolution based on partial optimization wasinvestigated. Based on the airspace congestion risk prediction, we established the airspace congestionresolution model. Through this model, we could comprehensively deal with several objectives,including the follow-up congestion, the total flight delay time, the different airspace users’ allocationequity and influence of the resolution strategies. Meanwhile, we designed high-dimensionalmultiobjective genetic NSGA2, realizing the initial optimization of the airspace congestion resolutionstrategies. The simulation results show that the model and algorithm established cannot only find outoptimum departure time and routes, but also reduce the risk of follow-up congestion, the operationcost and the influence, meanwhile, increase the equity of the airspace users.
(4) Methodology of airspace congestion risk resolution based on overal optimization wasresearched. First, in order to deal with the remaining problem of airspace congestion globaloptimization, we established the airspace congestion risk decision model, considering the distributionand balance of the air traffic flow, the workload of the air traffic controlors. Meanwhile, usingmulti-objective genetic algorithm, we reoptimized the airspace congestion risk resolution strategies.Combined with the airspace congestion resolution, we developed the airspace congestion riskevulation model, and utilized the decision tree to resolve the operating time of the airspace congestionresolution strategies and the threshold of the airspace congestion probability. Finally, the simulationresults show that the model and method established can deal with the air traffic global distribution andthe operation workload in each sector, and can obtain the suitable operating time and the airspacecongestion risk threshold.
Finally, in the thesis, we concluded the archievements of the probabilistic airspace congestionmanagement based on the uncertain traffic demand prediction, and pointed out the deficiency of thisresearch about microscopic traffic flow, human factors and performance system relization. Accordingto the conlusion above, we suggested the future study direction.
引文
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