空域分类关键技术及应用研究
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摘要
所谓空域分类关键技术就是以定性或定量的形式需要解决的空域分类过程中涉及的重点和难点问题,它是空域分类改革的核心技术,更是论文研究的主题。以该主题为核心,论文的研究范畴可划分为五大部分:空域分类方案及关键技术分析、空中交通流量的长期预测、空域保障系统的综合评估、空域分类安全性分析以及空域分类关键技术应用。第一部分是基础,第二、三、四部分是核心,第五部分是二、三、四部分研究的实例应用,这五部分构成一个彼此联系、完整统一的研究框架。
空域、空域分类及相关概念如何理解?国际民航组织(ICAO)空域分类标准及航空发达国家的空域分类方案如何?空域分类需要考虑哪些关键要素?进而涉及的关键技术有哪些?论文第一部分回答了这些问题,从而明确了研究重点,阐明了空域分类方案改革中必然要面对空中交通流量预测、空域保障系统综合评估以及空域安全性分析三个关键技术。
鉴于目前国内外对空中交通流量长期预测研究的不足,论文第二部分以全国飞机起降架次为例,根据1985年至2008年的历史数据,采用目前常用的时间序列、回归预测以及神经网络三种预测方法对中国民航空中交通量进行了适应性的比较分析;根据我国民航交通量数据少、增长速度快且不均衡的特点,基于GM(1,1)模型和最小二乘法原理,首次提出了我国空中交通量灰色组合长期预测方法;同时预测结果经过对比分析表明,灰组合预测模型在上述各种预测模型中预测精度最高,效果最好,应是现阶段我国空中交通量长期预测的首选模型。
通过分析通信、导航及监视等空域保障系统基本特点,根据ICAO相关标准及中国民用航空局的设备技术要求,论文第三部分分别建立了各种空域保障系统的性能评价指标体系,提出了空域保障系统性能模糊综合评价方法,并以雷达监视系统综合评价为例证实了该方法的可行性,给出了空域保障系统综合评估流程。
空域分类安全性研究关注了空域分类改革过程将涉及的安全问题:①空域分类方案的安全性分析;②低空空域飞行安全分析;③机场空域飞行安全分析;④空域分类人因安全分析。这4个方面构成了论文的第四部分内容。
空域分类方案是空域分类改革的前提条件。论文采用系统工程中“5M”模型,以空域分类方案安全性作为评价目标,从系统层面清理空域分类要素,将人、机、环、管理四个方面进行分解,分析了16个安全要素,建立了空域分类方案安全评价指标体系;基于D-S证据理论建立了空域分类方案安全性评价模型,对德尔斐专家调查法获取的数据进行处理。通过算例分析表明,评价指标体系与评价模型均实用且可行。
为了确定低空空域开放后该空域内航空器的飞行安全,基于国际民航组织标准和我国民航局规定,根据航空器动力学原理,论文采用看见避让(See and Avoid)原则,在飞行规则、能见度要求、反应时间、航空器速度以及盘旋坡度角或航空器爬升角度等约束条件下,建立了同高度对头飞行冲突和交叉飞行冲突的冲突避让轨迹数学模型。通过数值分析,结果表明低空空域航空器同高度对头相遇的安全避让需满足一定的飞行条件,而同高度交叉相遇飞行的航空器应该能安全解脱冲突。
机场空域安全性分析是目前国内外研究的空白。论文基于航班流服从泊松分布,根据航空器具有最小安全间隔要求,则机头时距服从移位负指数分布的特点,采用事件模型,对机场空域航空器的纵向飞行冲突风险分析进行了建模,给出了机场空域纵向飞行冲突定量分析方法。空域分类人因安全分析研究了不同空域类型的管制员与飞行员的可靠性。针对管制员安全
评估问题,论文首次将认知可靠性与失误分析(CREAM)方法应用于空中交通管制员人因可靠性定量分析,并基于灰色系统理论,将改进的三角白化权函数用于确定共同行为条件(CPC)各因子的水平等级,以减少主观性影响。对于非管制的低空空域,飞行员是空域安全的主体。论文根据人的认知可靠性(HCR)理论建立了飞行员反应失效概率模型,通过数值分析给出了不同飞行条件下的飞行员反应失效概率。
论文第五部分对空域分类的关键技术进行了应用研究。通过分析我国目前采用的空域分类方案的缺陷,根据我国国情以及借鉴航空发达国家空域分类经验,构建了我国空域分类初步方案框架,给出了各类空域划设标准。在提出空域分类方案的划设流程基础上,分别将空域分类关键技术--空中交通流量的长期预测、保障系统综合评估以及安全性分析具体应用于空域划设的过程中,并同时解决了该过程所涉及的空域结构确定等空域划设中的具体问题。
The so-called key technologies of airspace classification are the mathematic analysis models which solve related important problems and difficult problems by qualitative and quantitative methods. These are the key technologies of airspace classification reform; more importantly, they are the theme of the paper. Taking the theme as the core, the research categories of the paper can be classified into five parts: airspace classification projects and analysis of key technologies, the long-term forecast of air traffic flow, the comprehensive evaluation of air traffic support system, the security analysis of airspace classification and the key technologies of air space classification. The first part is foundation, the later three parts are the core and the last part is the application of the later three parts. The above five parts consists a mutually related and integrated research frame. What is the comprehension of airspace, airspace classification and relation concepts? What are
the standards of ICAO airspace classification and the airspace classification projects of developed-aviation countries? What are the key factors in the consideration of airspace classification and the involved key technologies? The first part answers these questions, and then research focuses are specified, followed by the illustration of three key technologies: the long-term forecast of air traffic flow, the comprehensive evaluation of air traffic support system, the security analysis of airspace classification which will be confronted in the reform of the air space classification project.
In view of the shortcomings in the present research on the long-term forecast of air traffic flow home and abroad, the second part of the paper takes the 1985-2008 data of national-wide airplane sortie as the example and makes an adaptable contrastive analysis on the Chinese air traffic flow by means of three frequently used forecast methods: time series forecast, regression forecast and neural-network forecast. According to the characteristics of our civil aviation which is lack of air traffic flow data and in rapid and uneven increase, the paper first puts forward the a gray combination forecast model for the national-wide air traffic flow on the base of the GM (1,1) model and the least squares principle. Meanwhile, by contrastive analysis, the result of forecast shows that the gray combination forecast model is of the highest forecast precision and of the best effect, which should be the first choice in our country’s long-term forecast models of air traffic flow at present.
By the analysis of the basic characteristics of air space support system such as communication, navigation and surveillance, based ICAO related standards and equipment technology requirement of Chinese civil aviation bureau, the third part sets up the capability evaluation index system of various air space support systems, puts forward the vague comprehensive evaluation method of airspace support system capability, verifies the feasibility of the method by the case of the comprehensive evaluation of radar surveillance system and presents the comprehensive evaluation flow of air space support system.
The security researches of airspace classification focus on safety problem which will be faced in the process of airspace classification reform:①security analysis of airspace classification reform;②flight security analysis in lower airspace;③flight security analysis on airport airspace;④human factor safety analysis of airspace classification, and all of which buildup the forth part of paper.
Airspace classification project is the precondition of air space classification reform. The paper adopts the“5M”model of systems engineering, sets the security of air space classification project as the mission, refines four aspects of the air space classification factors which are man, machine, media and management into 16 security factors and sets up the security evaluation index system of air space classification project. Based on D-S proof theory, the paper also sets up the security evaluation model of air space classification project and deals with the data achieved by the method of Delfa expert investigation. The case analysis denotes both the evaluation index system and the evaluation model are practical and feasible.
In order to determine the aircraft flight safety after the opening of low altitude airspace, based on the ICAO standards, CAAC regulations, and aerodynamic principles, following See and Avoid principle, under the binding conditions of flight rules, visibility requirements, responding time, the speed of aircraft, banking angle of circling and climbing angle, a mathematic collision avoidance model of converging traffic and intersecting traffic at the same level was established. The data analyses illustrate that there need be meet certain flight condition to avoid safely between the two head-to-head converging aircraft at the same level, while cross-converging aircraft at the same level can safely avoid the collisions.
The security analysis of airport airspace is blank in nowadays’research home and abroad. Since aircraft flow obeys the Poisson distribution, time-distance between aircraft heads obeys the shifted negative exponential distribution according to the requirement of the minimum safety distance between aircrafts, the paper builds the portrait aviation conflict risk model of airport air space aircrafts by the adoption of case model which presents the quantitative analysis method of airport air space portrait aviation conflict risk.
Air traffic controller and pilot reliabilities are researched in the part of human factors safety analysis of airspace classification. The security of airspace aviation lies mainly in the reliability of air traffic controller. According to the security evaluation issue of air traffic controller, the CREAM method is first applied to quantificationally analyze air traffic controller in this paper. Improved triangle whitenization weight function based on grey system theory is adopted to confirm level grade of each CPC gene and thus the subjective effect is reduced. Pilot is the main body of airspace safety in uncontrolled low airspace. Based on the human cognitive reliability (HCR) theory, the responding failure probability model of pilots was also established, and pilot’s responding failure probability is arrived after data analysis.
The application of airspace classification key technologies is researched in fifth part. Through the analysis on the weakness of Chinese present airspace classification project, according to Chinese situation and successful experience of developed-aviation countries’airspace classification, the primary project frame of our nation’s airspace classification is present and division standards of various airspaces is clarified. On the basis of the division flow of airspace classification project, the key airspace classification technologies which are the long-term forecast of air traffic flow, the comprehensive evaluation of air support system, the security analysis of airspace classification, are applied respectively to the airspace division process, along with the solution of the specific problems in the airspace division such as the clarify of airspace structure involved in the process.
空域、空域分类及相关概念如何理解?国际民航组织(ICAO)空域分类标准及航空发达国家的空域分类方案如何?空域分类需要考虑哪些关键要素?进而涉及的关键技术有哪些?论文第一部分回答了这些问题,从而明确了研究重点,阐明了空域分类方案改革中必然要面对空中交通流量预测、空域保障系统综合评估以及空域安全性分析三个关键技术。
鉴于目前国内外对空中交通流量长期预测研究的不足,论文第二部分以全国飞机起降架次为例,根据1985年至2008年的历史数据,采用目前常用的时间序列、回归预测以及神经网络三种预测方法对中国民航空中交通量进行了适应性的比较分析;根据我国民航交通量数据少、增长速度快且不均衡的特点,基于GM(1,1)模型和最小二乘法原理,首次提出了我国空中交通量灰色组合长期预测方法;同时预测结果经过对比分析表明,灰组合预测模型在上述各种预测模型中预测精度最高,效果最好,应是现阶段我国空中交通量长期预测的首选模型。
通过分析通信、导航及监视等空域保障系统基本特点,根据ICAO相关标准及中国民用航空局的设备技术要求,论文第三部分分别建立了各种空域保障系统的性能评价指标体系,提出了空域保障系统性能模糊综合评价方法,并以雷达监视系统综合评价为例证实了该方法的可行性,给出了空域保障系统综合评估流程。
空域分类安全性研究关注了空域分类改革过程将涉及的安全问题:①空域分类方案的安全性分析;②低空空域飞行安全分析;③机场空域飞行安全分析;④空域分类人因安全分析。这4个方面构成了论文的第四部分内容。
空域分类方案是空域分类改革的前提条件。论文采用系统工程中“5M”模型,以空域分类方案安全性作为评价目标,从系统层面清理空域分类要素,将人、机、环、管理四个方面进行分解,分析了16个安全要素,建立了空域分类方案安全评价指标体系;基于D-S证据理论建立了空域分类方案安全性评价模型,对德尔斐专家调查法获取的数据进行处理。通过算例分析表明,评价指标体系与评价模型均实用且可行。
为了确定低空空域开放后该空域内航空器的飞行安全,基于国际民航组织标准和我国民航局规定,根据航空器动力学原理,论文采用看见避让(See and Avoid)原则,在飞行规则、能见度要求、反应时间、航空器速度以及盘旋坡度角或航空器爬升角度等约束条件下,建立了同高度对头飞行冲突和交叉飞行冲突的冲突避让轨迹数学模型。通过数值分析,结果表明低空空域航空器同高度对头相遇的安全避让需满足一定的飞行条件,而同高度交叉相遇飞行的航空器应该能安全解脱冲突。
机场空域安全性分析是目前国内外研究的空白。论文基于航班流服从泊松分布,根据航空器具有最小安全间隔要求,则机头时距服从移位负指数分布的特点,采用事件模型,对机场空域航空器的纵向飞行冲突风险分析进行了建模,给出了机场空域纵向飞行冲突定量分析方法。空域分类人因安全分析研究了不同空域类型的管制员与飞行员的可靠性。针对管制员安全
评估问题,论文首次将认知可靠性与失误分析(CREAM)方法应用于空中交通管制员人因可靠性定量分析,并基于灰色系统理论,将改进的三角白化权函数用于确定共同行为条件(CPC)各因子的水平等级,以减少主观性影响。对于非管制的低空空域,飞行员是空域安全的主体。论文根据人的认知可靠性(HCR)理论建立了飞行员反应失效概率模型,通过数值分析给出了不同飞行条件下的飞行员反应失效概率。
论文第五部分对空域分类的关键技术进行了应用研究。通过分析我国目前采用的空域分类方案的缺陷,根据我国国情以及借鉴航空发达国家空域分类经验,构建了我国空域分类初步方案框架,给出了各类空域划设标准。在提出空域分类方案的划设流程基础上,分别将空域分类关键技术--空中交通流量的长期预测、保障系统综合评估以及安全性分析具体应用于空域划设的过程中,并同时解决了该过程所涉及的空域结构确定等空域划设中的具体问题。
The so-called key technologies of airspace classification are the mathematic analysis models which solve related important problems and difficult problems by qualitative and quantitative methods. These are the key technologies of airspace classification reform; more importantly, they are the theme of the paper. Taking the theme as the core, the research categories of the paper can be classified into five parts: airspace classification projects and analysis of key technologies, the long-term forecast of air traffic flow, the comprehensive evaluation of air traffic support system, the security analysis of airspace classification and the key technologies of air space classification. The first part is foundation, the later three parts are the core and the last part is the application of the later three parts. The above five parts consists a mutually related and integrated research frame. What is the comprehension of airspace, airspace classification and relation concepts? What are
the standards of ICAO airspace classification and the airspace classification projects of developed-aviation countries? What are the key factors in the consideration of airspace classification and the involved key technologies? The first part answers these questions, and then research focuses are specified, followed by the illustration of three key technologies: the long-term forecast of air traffic flow, the comprehensive evaluation of air traffic support system, the security analysis of airspace classification which will be confronted in the reform of the air space classification project.
In view of the shortcomings in the present research on the long-term forecast of air traffic flow home and abroad, the second part of the paper takes the 1985-2008 data of national-wide airplane sortie as the example and makes an adaptable contrastive analysis on the Chinese air traffic flow by means of three frequently used forecast methods: time series forecast, regression forecast and neural-network forecast. According to the characteristics of our civil aviation which is lack of air traffic flow data and in rapid and uneven increase, the paper first puts forward the a gray combination forecast model for the national-wide air traffic flow on the base of the GM (1,1) model and the least squares principle. Meanwhile, by contrastive analysis, the result of forecast shows that the gray combination forecast model is of the highest forecast precision and of the best effect, which should be the first choice in our country’s long-term forecast models of air traffic flow at present.
By the analysis of the basic characteristics of air space support system such as communication, navigation and surveillance, based ICAO related standards and equipment technology requirement of Chinese civil aviation bureau, the third part sets up the capability evaluation index system of various air space support systems, puts forward the vague comprehensive evaluation method of airspace support system capability, verifies the feasibility of the method by the case of the comprehensive evaluation of radar surveillance system and presents the comprehensive evaluation flow of air space support system.
The security researches of airspace classification focus on safety problem which will be faced in the process of airspace classification reform:①security analysis of airspace classification reform;②flight security analysis in lower airspace;③flight security analysis on airport airspace;④human factor safety analysis of airspace classification, and all of which buildup the forth part of paper.
Airspace classification project is the precondition of air space classification reform. The paper adopts the“5M”model of systems engineering, sets the security of air space classification project as the mission, refines four aspects of the air space classification factors which are man, machine, media and management into 16 security factors and sets up the security evaluation index system of air space classification project. Based on D-S proof theory, the paper also sets up the security evaluation model of air space classification project and deals with the data achieved by the method of Delfa expert investigation. The case analysis denotes both the evaluation index system and the evaluation model are practical and feasible.
In order to determine the aircraft flight safety after the opening of low altitude airspace, based on the ICAO standards, CAAC regulations, and aerodynamic principles, following See and Avoid principle, under the binding conditions of flight rules, visibility requirements, responding time, the speed of aircraft, banking angle of circling and climbing angle, a mathematic collision avoidance model of converging traffic and intersecting traffic at the same level was established. The data analyses illustrate that there need be meet certain flight condition to avoid safely between the two head-to-head converging aircraft at the same level, while cross-converging aircraft at the same level can safely avoid the collisions.
The security analysis of airport airspace is blank in nowadays’research home and abroad. Since aircraft flow obeys the Poisson distribution, time-distance between aircraft heads obeys the shifted negative exponential distribution according to the requirement of the minimum safety distance between aircrafts, the paper builds the portrait aviation conflict risk model of airport air space aircrafts by the adoption of case model which presents the quantitative analysis method of airport air space portrait aviation conflict risk.
Air traffic controller and pilot reliabilities are researched in the part of human factors safety analysis of airspace classification. The security of airspace aviation lies mainly in the reliability of air traffic controller. According to the security evaluation issue of air traffic controller, the CREAM method is first applied to quantificationally analyze air traffic controller in this paper. Improved triangle whitenization weight function based on grey system theory is adopted to confirm level grade of each CPC gene and thus the subjective effect is reduced. Pilot is the main body of airspace safety in uncontrolled low airspace. Based on the human cognitive reliability (HCR) theory, the responding failure probability model of pilots was also established, and pilot’s responding failure probability is arrived after data analysis.
The application of airspace classification key technologies is researched in fifth part. Through the analysis on the weakness of Chinese present airspace classification project, according to Chinese situation and successful experience of developed-aviation countries’airspace classification, the primary project frame of our nation’s airspace classification is present and division standards of various airspaces is clarified. On the basis of the division flow of airspace classification project, the key airspace classification technologies which are the long-term forecast of air traffic flow, the comprehensive evaluation of air support system, the security analysis of airspace classification, are applied respectively to the airspace division process, along with the solution of the specific problems in the airspace division such as the clarify of airspace structure involved in the process.
引文
[1]中国民用航空总局规划科技司.从统计看民航[M].北京:中国民航出版社, 1999-2010.
[2]杨元元.中国发展新一代民用航空运输系统的愿景[J].中国民用航空, 2007, 8: 16-18.
[3]中国民航局.全国民用机场布局规划[EB/OL], http://www.caac.gov.cn/B7/200802/ t20080202_11603.html.
[4] International Civil Aviation Organization. Annex 11 to the Convention on International Civil Aviation,Air Traffic Services[S]. Montreal: ICAO, 2001.
[5] Federal Aviation Administration. Airspace Designations and Reporting Points[S]. Washington: FAA, 2005.
[6] Federal Aviation Administration. Airspace Management Handbook[S]. Washington: FAA, 2005.
[7] Federal Aviation Administration. Order 7400.2F Procedures for Handling Airspace Matters[S]. Washington: FAA, 2006.
[8] European Organization for the Safety of Air Navigation. Eurocontrol Airspace Strategy for the ECAC States[S]. Brussels: 2001.
[9] European Organization for the Safety of Air Navigation. Eurocontrol Handbook for Airspace Management [S]. Brussels, 2003.
[10] European Organization for the Safety of Air Navigation. Eurocontrol Air Traffic Management Strategy for the Years 2000+[S]. Brussels, 2003.
[11] Civil Aviation Safety Authority of Australia. Airspace Designation and Standards[S]. Canberra: 1997.
[12] Civil Aviation Safety Authority of Korea. Aeronautical Information Publication[S]. Seoul: 2003.
[13] Civil Aviation Safety Authority of Japan. Aeronautical Information Publication[S]. Tokyo: 2007.
[14] Howard Grubb, Alexina Mason. Long Lead-time Forecasting of UK Air Passengers by Holt-Winters Methods with Damped Trend[J]. International Journal of Forecasting 2001, 17: 71-82.
[15]赵嶷飞,王红勇.空中交通流量的短期预测[J].交通运输工程与信息学报, 2007, 5(4): 23-28.
[16] Profillidis V A. Econometric and Fuzzy Models for the Forecast of Demand in the Airport of Rhodes[J]. Journal of Air Transport Management, 2000, 6(2): 95-100.
[17]樊玮,陈增强,袁著祉.基于C-均值聚类的航班预测模型[J].信息与控制, 2003, 12(6): 553-560.
[18]陈力华.我国民航客运市场的分析和预测[J].上海交通大学学报, 2003, 37(4): 623-625.
[19]魏新,冯兴杰.基于支持向量回归的机场旅客吞吐量预测[J].中国民航学院学报, 2004, 22(3): 45-47.
[20]赵玉环,郭爽.考虑随机因素的空中交通流量预测模型研究[J].中国民航大学学报, 2008, 26(4): 59-61.
[21] Devoto R, Farci C, Lilliu F. Analysis and Forecast of Air Transport Demand in Sardinia’s Airports as a Function of Tourism Variables[C]. Seville, the 8th International Conference on Urban Transport and the Environment in the 21st Centruy, 2002.
[22]王树明,夏国平.基于BP神经网络的飞行动态实时预测方法[J].北京航空航天大学学报, 2001, 27(6): 636-639.
[23] Cheng Taoya, Cui Deguang, Cheng Peng. Data Mining for Air Traffic Flow Forecasting: a Hybrid Model of Neural Network and Statistical Analysis[C]//Shanghai: IEEE ITSC2003, 2003.
[24]崔德光,吴淑宁,徐冰.空中交通流量预测的人工神经网络和回归组合方法[J].清华大学学报, 2005, 45(1): 96-99.
[25] Jianjun Meng, Zeqing Yang. Civil Aviation Passenger Traffic Volume Forecasting Based on Fuzzy Diagonal[C]. IMACS Multiconference on CESA, Beijing, 2006, 10.
[26] Blinova T O. Analysis of Possibility of Using Neural Network to Forecast Passenger Traffic Flows in Russia[J]. Aviation, 2007, 11(1): 28-35.
[27]张兆宁,郭爽.首都机场飞行流量的灰色区间预测[J].中国民航大学学报, 2007, 25(6): 1-4.
[28] Guo Shuang, Zhang Zhao Ning, Wang Lili, et al. The Gray-markov Model for Forecasting Air Traffic Flow Based on Residual Correction[C]. The 7th International Conference of Eastern Asia Society for Transportation Studies, Dalian: 2007.
[29] Chaouki Mustapha. Traffic Forecasts[R]. Cairo: Workshop on the Development of Business Case for the Implementation of CNS/ATM Systems, 2004.
[30] ICAO. Asia/Pacific Area Traffic Forecasts 2006–2020[R]. The Thirteenth Meeting of the Asia/Pacific Area Traffic Forecasting Group (APA TFG) Bangkok, 2006.
[31] FAA. Terminal Area Forecast[R]. FAA-APO-99-7, 1999.
[32] EUROCONTROL. Long Term Forecast Methodology[R]. EEC, 2007.
[33]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报, 2004, 7(2): 69-79.
[34] Hwang C L, Md Aasud A S. Multiple Objective Decision Making Methods and Applications[M]. Berlin: Spring-Verlag Press, 1979.
[35] Lichtenberg Fank R. Issues in Measuring Industrial R&D[J]. Research Policy,1990, 19(1): 157-163.
[36] Cooper W W, Tone K. Measures of Inefficiency in Data Envelopment Analysis and Stochastic Frontier Estimation[J]. European Journal of Operational Research, 1997, 2: 72-78.
[37]王军霞,官建成.复合DEA方法在测度企业知识管理绩效中的应用[J].科学学研究, 2002, 1: 84-88.
[38] Jacques Savoy. Statistical Inference in Retrieval Effectiveness Evaluation[J]. Information Processing and Management, 1997, 33(4): 495-512.
[39] Saaty TL. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process[M] . Princeton : RWS Publications, 1994.
[40] Ouyang Mingguang, Wang Weinong, Zhang Yuetao. A Fuzzy Comprehensive Evaluation Based Distributed Intrusion Detection[C]. Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, 2002: 281-284.
[41] Grabowski M R, Wallace WA. An Expert Systemfor Maritime Pilots: Its Design and Assessment Using Gaming[J]. Management Science, 1993, 3(12): 1506-1520.
[42] Hamiton, Nash, Pooch. Distributed Simulation[M]. Washington: CRC Press, 1997.
[43] Zhai Guofu, Chen Yinghua, Ren Wanbin. Random Vibration Analysis of Switching Apparatus Based on Monte Carlo Method[J]. Journal of Zhejiang University Science, 2007, 8(3): 422-425.
[44] Zheng Xuejun, Yang Bo, Zhu Zhe et al. Kinetic Monte Carlo Simulation of Growth of BaTiO3 Thin Film via Pulsed Laser Deposition[J]. Transactions of Nonferrous Metals Society of China, 2007: 1441-1446.
[45] Puccia C J, Levins R. Qualitative Modeling of Complex Systems[M]. Boston: Harvard University Press, 1985.
[46] Yan Sun, Zhen Sun. The Grey Comprehensive Evaluation Support System on Safety of Construction Sites[C]. Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, 2007, Nanjing, China: 273-278.
[47]朱祖平.产品概念交合分析的原理与案例研究[J].科研管理, 2000, 6: 16-20.
[48]程凌,潘彤,徐辉波.基于可拓学石化装置失效后果严重度评估方法研究[J].中国安全生产科学技术, 2009, 5(3): 185-189.
[49] Sun Wei, Niu Dongxiao, Shen Haiyu. Comprehensive Evaluation of Power Plants’Competition Ability with BP Neural Networks Method[C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005: 4641-4644.
[50]刘佳,魏彩乔,王西彬.粗糙集综合评价法在绿色制造评价中的应用研究[J].重庆环境科学, 2003, 25(12): 64-67.
[51]李俊芳,吴小萍.基于AHP-FUZZY多层次评判的城市轨道交通线网规划方案综合评价[J].武汉理工大学学报, 2007, 4(2): 205-208.
[52] Xu Weixiang, Liu Xumin. A Meta-Synthesis of Evaluating Complex Systems Based on Fuzzy Set and Rough Set[J]. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004: 1957-1961.
[53]宋如顺.基于小波神经网络的多属性决策方法及应用[J].控制与决策, 2000, 15(6): 765-768.
[54]张辉,高德利.基于模糊数学和灰色理论的多层次综合评价方法及其应用[J].数学的实践与认识, 2008, 38(3): 1-6.
[55]彭鹄.含风力发电的发输配电系统的可靠性综合评估[D].重庆:重庆大学, 2007.
[56]王伟.装备保障系统效能综合评估方法研究[D].长沙:国防科技大学, 2009.
[57]张超,马存宝,宋东等.基于故障树分析的航空电子系统BIT诊断策略设计[J].计算机测量与控制, 2008, 16(1): 12-16.
[58]张超,马存宝,宋东等.基于Vague故障树的航空电子系统可靠性分析[J].计算机工程, 2008, 34(7): 254-256.
[59]王斌,宋华.机载无线电通信系统故障树分析[J].航空维修与工程, 2009, 1: 52-54.
[60]张珏,樊晓光.机载电子设备智能故障诊断技术研究[J].测控技术, 2006, 29(19): 94-96.
[61]何晓薇.基于故障树分析方法的航空电子设备维护方案设计[J].航空维修与工程, 2003, 5: 34-35.
[62] Jasenka Rakas. Models For Air Traffic Management Performance During Equipment Outages[D]. Maryland University, 2001.
[63] Jasenka Rakas. Methodology For Estimating Terminal Airspace Service Availability And System Effectiveness[J]. AIAA's Aircraft Technology, Integration, and Operations (ATIO), 2002, Los Angeles, California, AIAA 2002-5815: 1-11.
[64] Jasenka Rakas, Wanjira Jirajaruporn, Mark Hansen. Models For The National Airspace System Infrastructure Performance[A]. AIAA 4th Aviation Technology, Integration and Operations (ATIO), 2004, Chicago, Illinois, AIAA 2004-6506: 1-10.
[65] Jasenka Rakas, Huifang Yin, Mark Hansen. Procedural and Operational Consequences of Navigational Equipment Outages: Exploration of Airport Performance[J]. Journal of Transportation Engineering ? ASCE, 2005, 10: 790-801.
[66] Jasenka Rakas, Huifang Yin. Modeling the Impact of Equipment Outages on National AirspaceSystem Operations[A]. AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO), 2005, Arlington, Virginia, AIAA2005-7360: 1-10.
[67] Gautam Gupta, Jasenka Rakas, Mark Hansen. Cost-effective Sampling to Evaluate Condition of Un-staffed Facilities in National Airspace System[A]. AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO), 2005, Arlington, Virginia, AIAA2005-7494: 1-9.
[68] Huifang Yin, Mark Hansen. System Impact of En Route Delays[A]. AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO), 2005, Arlington, Virginia, AIAA 2005-7484: 1-7.
[69] Rakas, Jasenka; Hecht, Myron. Airport Availability Modelling: a Different Perspective[J]. International Journal of Critical Infrastructures, 2007, 3(6): 430-456.
[70]唐颖.基于设备失效的机场及终端区运行性能影响研究[D].南京航空航天大学, 2008.
[71]杨尚文,戴福青.基于一种免疫遗传算法的自由飞冲突解脱[J].航空计算技术, 2007, 37 (1): 41-43.
[72]戴玲,夏学知.多Agent技术在飞行冲突解脱中的应用[J].舰船电子工程, 2008, 28(3): 62-64.
[73] James K, Jared C, Wynn C, et al. A Satisficing Approach to Aircraft Conflict Resolution[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 2008, 38(4): 510-521.
[74] Raghunathan U, Gopal V, Subramanian D, et al. 3D Conflict Resolution of Multiple Aircraft via Dynamic Optimization[C]. Paper NO. AIAA 2003-5675, AIAA Guidance, Navigation, and Control Conference, 2003.
[75] Lucia P, Eric M F, Antonio B. Conflict Resolution Problems for Air Traffic Management Systems Solved With Mixed Integer Programming [J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3(1): 3-11.
[76]程丽媛,韩松臣,刘星.采用内点约束的最优冲突解脱方法[J].交通运输工程学报, 2005, 5(2): 80-84.
[77]朱代武.低空空域飞行冲突避让算法[J].交通运输工程学报, 2005, 5(3): 73-76.
[78] Reich P G. Analysis of Long-range Air Traffic Systems: Separation Standards I[J]. Journal of the Institute of Navigation, 1966, 19(1): 88-89.
[79] Reich P G. Analysis of Long-range Air Traffic Systems: Separation Standards II[J]. Journal of the Institute of Navigation, 1966, 19(2): 169-186.
[80] Reich P G. Analysis of Long-range Air Traffic Systems: Separation Standards III[J]. Journal ofthe Institute of Navigation, 1966, 19(3): 331-347.
[81] Bakker G J, Blom H A P. Air Traffic Collision Risk Modeling[C]. Proceedings of the 32nd Conference on Decidon and Control, San Antonio, Texas, 1993: 1464-1469.
[82] Blom H A P, Bakker G J, Blander P J G, et al. Accident Risk Assessment for Advanced ATM[C]. Proceedings of the 2nd USA/Europe ATM R&D Seminar, Orlando, FL, 1998.
[83] Peter Brooker. Lateral Collision Risk in Air Traffic Systems: a‘Post-reich’Event Model[J]. Journal of Navigarion, 2003, 56(3): 399-409.
[84] Roger Shepherd, Rick Cassell, Rajeer Thapa, et al. A Reduced Aircraft Separation Risk Assessment Model[EB/OL]. http://www.airscene.com/whitepapers/A%20Reduced%20Aircraft %20Separation%20Risk.pdf.
[85] Rick Cassell, Alex Smith, Roger Shepherd, et al. A Risk Assessment Model for Free Flight-terminal Area Reduced Separation[EB/OL]. http://www.rannoch.com/pdf/dascrso.pdf.
[86]罗晓利. 1990～2003年中国民航152起小于间隔飞行事件的分类统计研究[J].中国安全科学学报, 2004, (14)12: 26-32.
[87] Swain A D, Guttmann H E. Handbook of Human Reliability Analysis with Emphasis on Nuclear Power PlantApplication[R]. NUREG/CR-1278, U.S. Nuclear Regulatory Commission, 1983.
[88] Hannaman G W, Spurgin A J, Lukic Y. Human Cognitive Reliability Model for PRA Analysis[S]. NUS24531, 1985.
[89] Hannaman G W, Spurgin A J, Lukic Y. A Model for Assessing Human Cognitive Reliability[C]//IEEE. Proceedings IEEE Conference on Human Factors and Power Plants. Monterey: IEEE, 1985: 343-353.
[90] Spurgin A J. Operator Reliability Experiments Using Power Plant Simulators[R]. PRI NP-6937 Vol 1.NUS Corporation, Maryland. 1990.
[91]王遥,高平校,沈祖培等.人的认知可靠性模型分类及实验研究[J].核动力工程, 2004, 25(6): 542-545.
[92] Hollnagel. Cognitive Reliability and Error Analysis Method–CREAM[M]. Amsterdam, New York: Elsevier, 1998: 245-255.
[93] Konstandinidou M, Nivolianitou Z, Kiranoudis C, et al. A Fuzzy Modeling Application of CREAM Methodology for Human Reliability Analysis[J]. Reliability Engineering and System Safety, 2006, 91(6): 706-716.
[94] Marseguerra M, Zio E, Librizzi M. Quantitative Developments in the Cognitive Reliability and Error Analysis Method (CREAM) for the Assessment of Human Performance[J]. Annals ofNuclear Energy, 2006, 10 (33): 894-910.
[95]高文宇,张力.人因可靠性分析方法CREAM及其应用研究[J].人类工效学, 2002, 8 (4): 8-12.
[96]王遥,沈祖培. CREAM—第二代人因可靠性分析方法[J].工业工程与管理, 2005, (3): 17-21.
[97]何云中,郭定.空中交通管制中人的可靠性模糊综合评价研究[J].中国安全科学学报, 2004, (14)11: 57-60.
[98]张军.现代空中交通管理[M].北京:北京航空航天大学出版社, 2005.
[99] International Civil Aviation Organization. Annex 11 to the Convention on International Civil Aviation, Air Traffic Services[S]. Montreal: ICAO, 2001.
[100]全国人民代表大会常务委员会. 56号令,中华人民共和国民用航空法[S].北京: 1995.
[101] International Civil Aviation Organization. Rules of the Air and Air Traffic Services[S]. Montreal: ICAO, 2007.
[102]中国民用航空总局. 122号令,民用航空使用空域办法[S].北京: 2004.
[103] airspace class—United Kingdom[EB/OL]. Wikipedia, 2010, 4, 2. http://en.wikipedia.org/wiki/ Airspace_class.
[104] airspace class—Germany[EB/OL]. Wikipedia, 2010, 4, 2. http://en.wikipedia.org/wiki/Airspace _ class.
[105]国务院、中央军委.中华人民共和国飞行基本规则[S].北京: 2007.
[106]张兆宁,王莉莉.空中交通流量管理理论与方法[M].北京:科学出版社, 2009.
[107]陈立华.中国民航客运市场的分析和预测[J].上海交通大学学报, 2003, 37(4): 623-625.
[108]叶舟,李忠民,李晓峰.中国民航发展与国民经济增长关系的实证分析[J].天津理工大学学报, 2005, 21(5): 81-84.
[109] J. C. Jan,Shih-Lin Hung, S. Y. Chi, et al. Neural Network Forecast Model in Deep Excavation[J].Journal of Computing in Civil Engineering, 2002, 16(1): 59~65.
[110] Siwei Lu,Ziqing Wang,Jun Shen. Nero-fuzzy Synergism to the Intelligent System for Edge Detection and Enhancement[J]. Pattem Recognition, 2003, 36: 2395-2409.
[111] S. H. Ong, N. C. Yeo,K. H. Lee, Y. V. Venkatesh, et al. Segmentation of Color Images Using a Two-stage Self-organizing Network[J]. Image and Vision Computing, 2002, 20: 279-289.
[112] C. Yuan, H. Niemann. Neural Networks for the Recognition and Pose Estimation of 3D Objects from a Single 2D Perspective View[J]. Image and Vision Computing, 2001, 19: 585-592.
[113] Shih-Wen Hsiao, Hung-Cheng Tsai. Use of Gray System Theory in Product-Color Planning[J]. Color Research and Application, 2004, 29(3): 222-231.
[114] Yonghong Hao, Tian-Chyi J. Yeh, Yanrong Wanag, et al. Analysis of Karst Aquifer Spring Flows with a Gray System Decomposition Model[J]. Ground Water, 2007, 45(1): 46-52.
[115] J. Perf, Constr, fac. Gray System Model for Estimating the Pavement International Roughness Index[J]. Journal of Performance of Constructed Facilities, 2005, 19(1):62-68.
[116]徐兰芳,胡怀飞,桑子夏等.基于灰色理论的信誉报告机制[J].软件学报, 18(7): 1730-1737.
[117]江志华.灰色预测模型GM(1,1)及其在交通运量预测中的应用[J].武汉理工大学学报(交通科学与工程版), 2004, 28(2): 305-307.
[118]刘思峰,谢乃明.灰色系统理论及其应用(第四版)[M].北京:科学出版社, 2008.
[119]宁宣熙,刘思峰.管理预测与决策方法[M].北京:科学出版社, 2003.
[120]陈立萍.统计建模与R软件[M].北京:清华大学出版社, 2007.
[121] International Civil Aviation Organization. Annex 10 to the Convention on International Civil Aviation, Volume III Communication Systems[S]. Montreal: ICAO, 1995.
[122] International Civil Aviation Organization. Annex 10 to the Convention on International Civil Aviation, Volume I Radio Navigation Aids[S]. Montreal: ICAO, 1996.
[123]杜栋,庞庆华.现代综合评价方法与案例精选[M].北京:清华大学出版社, 2005.
[124]中国民用航空总局.甚高频地空通信地面设备通用规范第1部分:甚高频设备技术要求[R]. MH 4001.1-1995.
[125]中国民用航空总局.航空无线电导航设备第2部分:甚高频全向信标(VOR)技术要求[R]. MH/T4006.2-1998.
[126]中国民用航空总局.航空无线电导航设备第3部分:测距仪(DME)技术要求[R]. MH/T4006.3-1998.
[127] Michael W Mcveigh, David Drew. Accuracy of Position Data Using Mosaic Techniques[J]. Journal of ATC, 2001, 1: 7-18.
[128]张彦举.系统评价方法研究[D].南京:河海大学, 2005.
[129] Michael Hanss. Applied Fuzzy Arithmetic[M]. Berlin: Springer Berlin Heidelberg, 2005.
[130]王增和,卢春兰,钱祖平.天线与电波传播[M].北京:机械工业出版社, 2003.
[131] International Civil Aviation Organization. Annex 10 to the Convention on International Civil Aviation, Volume IV Surveillance Radar and Collision Avoidance Systems[S]. Montreal: ICAO, 1998.
[132] Eurocontrol. Eurocontrol Standard Document for Radar Surveillance in En-route Airspace and Major Terminal Areas[S]. SUR.ET1.ST01.1000-STD-01-01. Brussels: Eurocontrol, 1997.
[133]张乃禄,刘灿.安全评价技术[M].西安:西安电子科技大学出版社, 2007.
[134] FAA. System Safety Handbook[R]. Washington: FAA, 2000.
[135] Tian Junfeng, Weidong Zhao, Du Ruizhong. D-S Evidence Theory and Its Data Fusion Application in Intrusion Detection[M]. Berlin Heidelberg: Springer Berlin Heidelberg, 2005, 244-251.
[136]叶清,吴晓平,宋业新.基于权重系数与冲突概率重新分配的证据合成方法[J].系统工程与电子技术, 2006, 28(7): 1014-1016.
[137]朱强,高金华,蒋凤伟.证据理论在机场安全评估中的应用[J].中国民航飞行学院学报, 2008, 19(3): 21-25.
[138] International Civil Aviation Organization. Annex 6 Operation of Aircraft[S]. Montreal: ICAO, 2001.
[139]国务院中央军委空中交通管制委员会.飞行间隔规定[S].北京: 2002.
[140]方振平,陈万春,张曙光.航空飞行器飞行动力学[M].北京:北京航空航天大学出版社, 2005: 102-113.
[141]刘卫华.利用THERP与HCR相结合的方法对秦山三期PSA人因可靠性进行修正[D].上海:上海交通大学机械与动力工程学院, 2007.
[142]中国民用航空总局空中交通管理局.飞机性能数据手册[S].北京:中国民用航空总局, 2000.
[143] Air Traffic Organization. Safety Management System Manual[S]. Washington: FAA, 2008.
[144]钟育鸣,韩松臣,张旭婧.机场容量评估中仿真飞机流的设计与实现[J].交通与计算机, 2008, 26(6): 120-123.
[145]李冬宾.飞行间隔安全评估模型和方法研究[D].南京:南京航空航天大学, 2008.
[146]隋东.国家空域规划与分类标准研究[R].北京:国家空中交通管制委员会, 2008.
[147]中国民用航空总局.中国民航国内航空资料汇编机场2华东[S].北京: CAAC, 2006.
[148]中国民用航空总局.国内航空资料汇编航图手册机场3华东[S].北京: CAAC, 2006.
[149]韩松臣,机场密集区域的飞行终端区规划研究[R].北京:国家空管委, 2009.
[150] Geert Moek, Edward Lutz, William Mosberg. Risk Assessment of RNP10 and RVSM in the South Atlantic Flight Identification Regions[R]. America, 2001.
[2]杨元元.中国发展新一代民用航空运输系统的愿景[J].中国民用航空, 2007, 8: 16-18.
[3]中国民航局.全国民用机场布局规划[EB/OL], http://www.caac.gov.cn/B7/200802/ t20080202_11603.html.
[4] International Civil Aviation Organization. Annex 11 to the Convention on International Civil Aviation,Air Traffic Services[S]. Montreal: ICAO, 2001.
[5] Federal Aviation Administration. Airspace Designations and Reporting Points[S]. Washington: FAA, 2005.
[6] Federal Aviation Administration. Airspace Management Handbook[S]. Washington: FAA, 2005.
[7] Federal Aviation Administration. Order 7400.2F Procedures for Handling Airspace Matters[S]. Washington: FAA, 2006.
[8] European Organization for the Safety of Air Navigation. Eurocontrol Airspace Strategy for the ECAC States[S]. Brussels: 2001.
[9] European Organization for the Safety of Air Navigation. Eurocontrol Handbook for Airspace Management [S]. Brussels, 2003.
[10] European Organization for the Safety of Air Navigation. Eurocontrol Air Traffic Management Strategy for the Years 2000+[S]. Brussels, 2003.
[11] Civil Aviation Safety Authority of Australia. Airspace Designation and Standards[S]. Canberra: 1997.
[12] Civil Aviation Safety Authority of Korea. Aeronautical Information Publication[S]. Seoul: 2003.
[13] Civil Aviation Safety Authority of Japan. Aeronautical Information Publication[S]. Tokyo: 2007.
[14] Howard Grubb, Alexina Mason. Long Lead-time Forecasting of UK Air Passengers by Holt-Winters Methods with Damped Trend[J]. International Journal of Forecasting 2001, 17: 71-82.
[15]赵嶷飞,王红勇.空中交通流量的短期预测[J].交通运输工程与信息学报, 2007, 5(4): 23-28.
[16] Profillidis V A. Econometric and Fuzzy Models for the Forecast of Demand in the Airport of Rhodes[J]. Journal of Air Transport Management, 2000, 6(2): 95-100.
[17]樊玮,陈增强,袁著祉.基于C-均值聚类的航班预测模型[J].信息与控制, 2003, 12(6): 553-560.
[18]陈力华.我国民航客运市场的分析和预测[J].上海交通大学学报, 2003, 37(4): 623-625.
[19]魏新,冯兴杰.基于支持向量回归的机场旅客吞吐量预测[J].中国民航学院学报, 2004, 22(3): 45-47.
[20]赵玉环,郭爽.考虑随机因素的空中交通流量预测模型研究[J].中国民航大学学报, 2008, 26(4): 59-61.
[21] Devoto R, Farci C, Lilliu F. Analysis and Forecast of Air Transport Demand in Sardinia’s Airports as a Function of Tourism Variables[C]. Seville, the 8th International Conference on Urban Transport and the Environment in the 21st Centruy, 2002.
[22]王树明,夏国平.基于BP神经网络的飞行动态实时预测方法[J].北京航空航天大学学报, 2001, 27(6): 636-639.
[23] Cheng Taoya, Cui Deguang, Cheng Peng. Data Mining for Air Traffic Flow Forecasting: a Hybrid Model of Neural Network and Statistical Analysis[C]//Shanghai: IEEE ITSC2003, 2003.
[24]崔德光,吴淑宁,徐冰.空中交通流量预测的人工神经网络和回归组合方法[J].清华大学学报, 2005, 45(1): 96-99.
[25] Jianjun Meng, Zeqing Yang. Civil Aviation Passenger Traffic Volume Forecasting Based on Fuzzy Diagonal[C]. IMACS Multiconference on CESA, Beijing, 2006, 10.
[26] Blinova T O. Analysis of Possibility of Using Neural Network to Forecast Passenger Traffic Flows in Russia[J]. Aviation, 2007, 11(1): 28-35.
[27]张兆宁,郭爽.首都机场飞行流量的灰色区间预测[J].中国民航大学学报, 2007, 25(6): 1-4.
[28] Guo Shuang, Zhang Zhao Ning, Wang Lili, et al. The Gray-markov Model for Forecasting Air Traffic Flow Based on Residual Correction[C]. The 7th International Conference of Eastern Asia Society for Transportation Studies, Dalian: 2007.
[29] Chaouki Mustapha. Traffic Forecasts[R]. Cairo: Workshop on the Development of Business Case for the Implementation of CNS/ATM Systems, 2004.
[30] ICAO. Asia/Pacific Area Traffic Forecasts 2006–2020[R]. The Thirteenth Meeting of the Asia/Pacific Area Traffic Forecasting Group (APA TFG) Bangkok, 2006.
[31] FAA. Terminal Area Forecast[R]. FAA-APO-99-7, 1999.
[32] EUROCONTROL. Long Term Forecast Methodology[R]. EEC, 2007.
[33]陈衍泰,陈国宏,李美娟.综合评价方法分类及研究进展[J].管理科学学报, 2004, 7(2): 69-79.
[34] Hwang C L, Md Aasud A S. Multiple Objective Decision Making Methods and Applications[M]. Berlin: Spring-Verlag Press, 1979.
[35] Lichtenberg Fank R. Issues in Measuring Industrial R&D[J]. Research Policy,1990, 19(1): 157-163.
[36] Cooper W W, Tone K. Measures of Inefficiency in Data Envelopment Analysis and Stochastic Frontier Estimation[J]. European Journal of Operational Research, 1997, 2: 72-78.
[37]王军霞,官建成.复合DEA方法在测度企业知识管理绩效中的应用[J].科学学研究, 2002, 1: 84-88.
[38] Jacques Savoy. Statistical Inference in Retrieval Effectiveness Evaluation[J]. Information Processing and Management, 1997, 33(4): 495-512.
[39] Saaty TL. Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process[M] . Princeton : RWS Publications, 1994.
[40] Ouyang Mingguang, Wang Weinong, Zhang Yuetao. A Fuzzy Comprehensive Evaluation Based Distributed Intrusion Detection[C]. Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, 2002: 281-284.
[41] Grabowski M R, Wallace WA. An Expert Systemfor Maritime Pilots: Its Design and Assessment Using Gaming[J]. Management Science, 1993, 3(12): 1506-1520.
[42] Hamiton, Nash, Pooch. Distributed Simulation[M]. Washington: CRC Press, 1997.
[43] Zhai Guofu, Chen Yinghua, Ren Wanbin. Random Vibration Analysis of Switching Apparatus Based on Monte Carlo Method[J]. Journal of Zhejiang University Science, 2007, 8(3): 422-425.
[44] Zheng Xuejun, Yang Bo, Zhu Zhe et al. Kinetic Monte Carlo Simulation of Growth of BaTiO3 Thin Film via Pulsed Laser Deposition[J]. Transactions of Nonferrous Metals Society of China, 2007: 1441-1446.
[45] Puccia C J, Levins R. Qualitative Modeling of Complex Systems[M]. Boston: Harvard University Press, 1985.
[46] Yan Sun, Zhen Sun. The Grey Comprehensive Evaluation Support System on Safety of Construction Sites[C]. Proceedings of 2007 IEEE International Conference on Grey Systems and Intelligent Services, 2007, Nanjing, China: 273-278.
[47]朱祖平.产品概念交合分析的原理与案例研究[J].科研管理, 2000, 6: 16-20.
[48]程凌,潘彤,徐辉波.基于可拓学石化装置失效后果严重度评估方法研究[J].中国安全生产科学技术, 2009, 5(3): 185-189.
[49] Sun Wei, Niu Dongxiao, Shen Haiyu. Comprehensive Evaluation of Power Plants’Competition Ability with BP Neural Networks Method[C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005: 4641-4644.
[50]刘佳,魏彩乔,王西彬.粗糙集综合评价法在绿色制造评价中的应用研究[J].重庆环境科学, 2003, 25(12): 64-67.
[51]李俊芳,吴小萍.基于AHP-FUZZY多层次评判的城市轨道交通线网规划方案综合评价[J].武汉理工大学学报, 2007, 4(2): 205-208.
[52] Xu Weixiang, Liu Xumin. A Meta-Synthesis of Evaluating Complex Systems Based on Fuzzy Set and Rough Set[J]. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, 2004: 1957-1961.
[53]宋如顺.基于小波神经网络的多属性决策方法及应用[J].控制与决策, 2000, 15(6): 765-768.
[54]张辉,高德利.基于模糊数学和灰色理论的多层次综合评价方法及其应用[J].数学的实践与认识, 2008, 38(3): 1-6.
[55]彭鹄.含风力发电的发输配电系统的可靠性综合评估[D].重庆:重庆大学, 2007.
[56]王伟.装备保障系统效能综合评估方法研究[D].长沙:国防科技大学, 2009.
[57]张超,马存宝,宋东等.基于故障树分析的航空电子系统BIT诊断策略设计[J].计算机测量与控制, 2008, 16(1): 12-16.
[58]张超,马存宝,宋东等.基于Vague故障树的航空电子系统可靠性分析[J].计算机工程, 2008, 34(7): 254-256.
[59]王斌,宋华.机载无线电通信系统故障树分析[J].航空维修与工程, 2009, 1: 52-54.
[60]张珏,樊晓光.机载电子设备智能故障诊断技术研究[J].测控技术, 2006, 29(19): 94-96.
[61]何晓薇.基于故障树分析方法的航空电子设备维护方案设计[J].航空维修与工程, 2003, 5: 34-35.
[62] Jasenka Rakas. Models For Air Traffic Management Performance During Equipment Outages[D]. Maryland University, 2001.
[63] Jasenka Rakas. Methodology For Estimating Terminal Airspace Service Availability And System Effectiveness[J]. AIAA's Aircraft Technology, Integration, and Operations (ATIO), 2002, Los Angeles, California, AIAA 2002-5815: 1-11.
[64] Jasenka Rakas, Wanjira Jirajaruporn, Mark Hansen. Models For The National Airspace System Infrastructure Performance[A]. AIAA 4th Aviation Technology, Integration and Operations (ATIO), 2004, Chicago, Illinois, AIAA 2004-6506: 1-10.
[65] Jasenka Rakas, Huifang Yin, Mark Hansen. Procedural and Operational Consequences of Navigational Equipment Outages: Exploration of Airport Performance[J]. Journal of Transportation Engineering ? ASCE, 2005, 10: 790-801.
[66] Jasenka Rakas, Huifang Yin. Modeling the Impact of Equipment Outages on National AirspaceSystem Operations[A]. AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO), 2005, Arlington, Virginia, AIAA2005-7360: 1-10.
[67] Gautam Gupta, Jasenka Rakas, Mark Hansen. Cost-effective Sampling to Evaluate Condition of Un-staffed Facilities in National Airspace System[A]. AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO), 2005, Arlington, Virginia, AIAA2005-7494: 1-9.
[68] Huifang Yin, Mark Hansen. System Impact of En Route Delays[A]. AIAA 5th Aviation, Technology, Integration, and Operations Conference (ATIO), 2005, Arlington, Virginia, AIAA 2005-7484: 1-7.
[69] Rakas, Jasenka; Hecht, Myron. Airport Availability Modelling: a Different Perspective[J]. International Journal of Critical Infrastructures, 2007, 3(6): 430-456.
[70]唐颖.基于设备失效的机场及终端区运行性能影响研究[D].南京航空航天大学, 2008.
[71]杨尚文,戴福青.基于一种免疫遗传算法的自由飞冲突解脱[J].航空计算技术, 2007, 37 (1): 41-43.
[72]戴玲,夏学知.多Agent技术在飞行冲突解脱中的应用[J].舰船电子工程, 2008, 28(3): 62-64.
[73] James K, Jared C, Wynn C, et al. A Satisficing Approach to Aircraft Conflict Resolution[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 2008, 38(4): 510-521.
[74] Raghunathan U, Gopal V, Subramanian D, et al. 3D Conflict Resolution of Multiple Aircraft via Dynamic Optimization[C]. Paper NO. AIAA 2003-5675, AIAA Guidance, Navigation, and Control Conference, 2003.
[75] Lucia P, Eric M F, Antonio B. Conflict Resolution Problems for Air Traffic Management Systems Solved With Mixed Integer Programming [J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3(1): 3-11.
[76]程丽媛,韩松臣,刘星.采用内点约束的最优冲突解脱方法[J].交通运输工程学报, 2005, 5(2): 80-84.
[77]朱代武.低空空域飞行冲突避让算法[J].交通运输工程学报, 2005, 5(3): 73-76.
[78] Reich P G. Analysis of Long-range Air Traffic Systems: Separation Standards I[J]. Journal of the Institute of Navigation, 1966, 19(1): 88-89.
[79] Reich P G. Analysis of Long-range Air Traffic Systems: Separation Standards II[J]. Journal of the Institute of Navigation, 1966, 19(2): 169-186.
[80] Reich P G. Analysis of Long-range Air Traffic Systems: Separation Standards III[J]. Journal ofthe Institute of Navigation, 1966, 19(3): 331-347.
[81] Bakker G J, Blom H A P. Air Traffic Collision Risk Modeling[C]. Proceedings of the 32nd Conference on Decidon and Control, San Antonio, Texas, 1993: 1464-1469.
[82] Blom H A P, Bakker G J, Blander P J G, et al. Accident Risk Assessment for Advanced ATM[C]. Proceedings of the 2nd USA/Europe ATM R&D Seminar, Orlando, FL, 1998.
[83] Peter Brooker. Lateral Collision Risk in Air Traffic Systems: a‘Post-reich’Event Model[J]. Journal of Navigarion, 2003, 56(3): 399-409.
[84] Roger Shepherd, Rick Cassell, Rajeer Thapa, et al. A Reduced Aircraft Separation Risk Assessment Model[EB/OL]. http://www.airscene.com/whitepapers/A%20Reduced%20Aircraft %20Separation%20Risk.pdf.
[85] Rick Cassell, Alex Smith, Roger Shepherd, et al. A Risk Assessment Model for Free Flight-terminal Area Reduced Separation[EB/OL]. http://www.rannoch.com/pdf/dascrso.pdf.
[86]罗晓利. 1990～2003年中国民航152起小于间隔飞行事件的分类统计研究[J].中国安全科学学报, 2004, (14)12: 26-32.
[87] Swain A D, Guttmann H E. Handbook of Human Reliability Analysis with Emphasis on Nuclear Power PlantApplication[R]. NUREG/CR-1278, U.S. Nuclear Regulatory Commission, 1983.
[88] Hannaman G W, Spurgin A J, Lukic Y. Human Cognitive Reliability Model for PRA Analysis[S]. NUS24531, 1985.
[89] Hannaman G W, Spurgin A J, Lukic Y. A Model for Assessing Human Cognitive Reliability[C]//IEEE. Proceedings IEEE Conference on Human Factors and Power Plants. Monterey: IEEE, 1985: 343-353.
[90] Spurgin A J. Operator Reliability Experiments Using Power Plant Simulators[R]. PRI NP-6937 Vol 1.NUS Corporation, Maryland. 1990.
[91]王遥,高平校,沈祖培等.人的认知可靠性模型分类及实验研究[J].核动力工程, 2004, 25(6): 542-545.
[92] Hollnagel. Cognitive Reliability and Error Analysis Method–CREAM[M]. Amsterdam, New York: Elsevier, 1998: 245-255.
[93] Konstandinidou M, Nivolianitou Z, Kiranoudis C, et al. A Fuzzy Modeling Application of CREAM Methodology for Human Reliability Analysis[J]. Reliability Engineering and System Safety, 2006, 91(6): 706-716.
[94] Marseguerra M, Zio E, Librizzi M. Quantitative Developments in the Cognitive Reliability and Error Analysis Method (CREAM) for the Assessment of Human Performance[J]. Annals ofNuclear Energy, 2006, 10 (33): 894-910.
[95]高文宇,张力.人因可靠性分析方法CREAM及其应用研究[J].人类工效学, 2002, 8 (4): 8-12.
[96]王遥,沈祖培. CREAM—第二代人因可靠性分析方法[J].工业工程与管理, 2005, (3): 17-21.
[97]何云中,郭定.空中交通管制中人的可靠性模糊综合评价研究[J].中国安全科学学报, 2004, (14)11: 57-60.
[98]张军.现代空中交通管理[M].北京:北京航空航天大学出版社, 2005.
[99] International Civil Aviation Organization. Annex 11 to the Convention on International Civil Aviation, Air Traffic Services[S]. Montreal: ICAO, 2001.
[100]全国人民代表大会常务委员会. 56号令,中华人民共和国民用航空法[S].北京: 1995.
[101] International Civil Aviation Organization. Rules of the Air and Air Traffic Services[S]. Montreal: ICAO, 2007.
[102]中国民用航空总局. 122号令,民用航空使用空域办法[S].北京: 2004.
[103] airspace class—United Kingdom[EB/OL]. Wikipedia, 2010, 4, 2. http://en.wikipedia.org/wiki/ Airspace_class.
[104] airspace class—Germany[EB/OL]. Wikipedia, 2010, 4, 2. http://en.wikipedia.org/wiki/Airspace _ class.
[105]国务院、中央军委.中华人民共和国飞行基本规则[S].北京: 2007.
[106]张兆宁,王莉莉.空中交通流量管理理论与方法[M].北京:科学出版社, 2009.
[107]陈立华.中国民航客运市场的分析和预测[J].上海交通大学学报, 2003, 37(4): 623-625.
[108]叶舟,李忠民,李晓峰.中国民航发展与国民经济增长关系的实证分析[J].天津理工大学学报, 2005, 21(5): 81-84.
[109] J. C. Jan,Shih-Lin Hung, S. Y. Chi, et al. Neural Network Forecast Model in Deep Excavation[J].Journal of Computing in Civil Engineering, 2002, 16(1): 59~65.
[110] Siwei Lu,Ziqing Wang,Jun Shen. Nero-fuzzy Synergism to the Intelligent System for Edge Detection and Enhancement[J]. Pattem Recognition, 2003, 36: 2395-2409.
[111] S. H. Ong, N. C. Yeo,K. H. Lee, Y. V. Venkatesh, et al. Segmentation of Color Images Using a Two-stage Self-organizing Network[J]. Image and Vision Computing, 2002, 20: 279-289.
[112] C. Yuan, H. Niemann. Neural Networks for the Recognition and Pose Estimation of 3D Objects from a Single 2D Perspective View[J]. Image and Vision Computing, 2001, 19: 585-592.
[113] Shih-Wen Hsiao, Hung-Cheng Tsai. Use of Gray System Theory in Product-Color Planning[J]. Color Research and Application, 2004, 29(3): 222-231.
[114] Yonghong Hao, Tian-Chyi J. Yeh, Yanrong Wanag, et al. Analysis of Karst Aquifer Spring Flows with a Gray System Decomposition Model[J]. Ground Water, 2007, 45(1): 46-52.
[115] J. Perf, Constr, fac. Gray System Model for Estimating the Pavement International Roughness Index[J]. Journal of Performance of Constructed Facilities, 2005, 19(1):62-68.
[116]徐兰芳,胡怀飞,桑子夏等.基于灰色理论的信誉报告机制[J].软件学报, 18(7): 1730-1737.
[117]江志华.灰色预测模型GM(1,1)及其在交通运量预测中的应用[J].武汉理工大学学报(交通科学与工程版), 2004, 28(2): 305-307.
[118]刘思峰,谢乃明.灰色系统理论及其应用(第四版)[M].北京:科学出版社, 2008.
[119]宁宣熙,刘思峰.管理预测与决策方法[M].北京:科学出版社, 2003.
[120]陈立萍.统计建模与R软件[M].北京:清华大学出版社, 2007.
[121] International Civil Aviation Organization. Annex 10 to the Convention on International Civil Aviation, Volume III Communication Systems[S]. Montreal: ICAO, 1995.
[122] International Civil Aviation Organization. Annex 10 to the Convention on International Civil Aviation, Volume I Radio Navigation Aids[S]. Montreal: ICAO, 1996.
[123]杜栋,庞庆华.现代综合评价方法与案例精选[M].北京:清华大学出版社, 2005.
[124]中国民用航空总局.甚高频地空通信地面设备通用规范第1部分:甚高频设备技术要求[R]. MH 4001.1-1995.
[125]中国民用航空总局.航空无线电导航设备第2部分:甚高频全向信标(VOR)技术要求[R]. MH/T4006.2-1998.
[126]中国民用航空总局.航空无线电导航设备第3部分:测距仪(DME)技术要求[R]. MH/T4006.3-1998.
[127] Michael W Mcveigh, David Drew. Accuracy of Position Data Using Mosaic Techniques[J]. Journal of ATC, 2001, 1: 7-18.
[128]张彦举.系统评价方法研究[D].南京:河海大学, 2005.
[129] Michael Hanss. Applied Fuzzy Arithmetic[M]. Berlin: Springer Berlin Heidelberg, 2005.
[130]王增和,卢春兰,钱祖平.天线与电波传播[M].北京:机械工业出版社, 2003.
[131] International Civil Aviation Organization. Annex 10 to the Convention on International Civil Aviation, Volume IV Surveillance Radar and Collision Avoidance Systems[S]. Montreal: ICAO, 1998.
[132] Eurocontrol. Eurocontrol Standard Document for Radar Surveillance in En-route Airspace and Major Terminal Areas[S]. SUR.ET1.ST01.1000-STD-01-01. Brussels: Eurocontrol, 1997.
[133]张乃禄,刘灿.安全评价技术[M].西安:西安电子科技大学出版社, 2007.
[134] FAA. System Safety Handbook[R]. Washington: FAA, 2000.
[135] Tian Junfeng, Weidong Zhao, Du Ruizhong. D-S Evidence Theory and Its Data Fusion Application in Intrusion Detection[M]. Berlin Heidelberg: Springer Berlin Heidelberg, 2005, 244-251.
[136]叶清,吴晓平,宋业新.基于权重系数与冲突概率重新分配的证据合成方法[J].系统工程与电子技术, 2006, 28(7): 1014-1016.
[137]朱强,高金华,蒋凤伟.证据理论在机场安全评估中的应用[J].中国民航飞行学院学报, 2008, 19(3): 21-25.
[138] International Civil Aviation Organization. Annex 6 Operation of Aircraft[S]. Montreal: ICAO, 2001.
[139]国务院中央军委空中交通管制委员会.飞行间隔规定[S].北京: 2002.
[140]方振平,陈万春,张曙光.航空飞行器飞行动力学[M].北京:北京航空航天大学出版社, 2005: 102-113.
[141]刘卫华.利用THERP与HCR相结合的方法对秦山三期PSA人因可靠性进行修正[D].上海:上海交通大学机械与动力工程学院, 2007.
[142]中国民用航空总局空中交通管理局.飞机性能数据手册[S].北京:中国民用航空总局, 2000.
[143] Air Traffic Organization. Safety Management System Manual[S]. Washington: FAA, 2008.
[144]钟育鸣,韩松臣,张旭婧.机场容量评估中仿真飞机流的设计与实现[J].交通与计算机, 2008, 26(6): 120-123.
[145]李冬宾.飞行间隔安全评估模型和方法研究[D].南京:南京航空航天大学, 2008.
[146]隋东.国家空域规划与分类标准研究[R].北京:国家空中交通管制委员会, 2008.
[147]中国民用航空总局.中国民航国内航空资料汇编机场2华东[S].北京: CAAC, 2006.
[148]中国民用航空总局.国内航空资料汇编航图手册机场3华东[S].北京: CAAC, 2006.
[149]韩松臣,机场密集区域的飞行终端区规划研究[R].北京:国家空管委, 2009.
[150] Geert Moek, Edward Lutz, William Mosberg. Risk Assessment of RNP10 and RVSM in the South Atlantic Flight Identification Regions[R]. America, 2001.