天气影响的机场容量与延误评估研究
|
摘要
恶劣天气是导致机场容量下降的主要原因。由于天气复杂多变，使得天气对容量的影响具有明显的随机性、实时性和动态性特点。鉴于此，大部分相关研究并未在模型中直接加入天气影响因素。随着概率交通流量管理的提出和发展，要求将天气信息加入流量管理系统中，并提供有效的容量分布和概率作为关键的决策依据。因此，天气影响的容量评估成为空中交通流量管理迫切需要解决的关键问题。另一方面，随着航空运输高速发展，恶劣天气造成的容量与需求不平衡问题愈发凸显，成为导致航班延误的重要原因。为了提供合理的流量控制决策和有效的事后运行分析，有必要建立延误模型描述天气和机场延误之间的关系，并建立评估延误的性能基准，将延误放到相似天气和需求影响因素下进行评估。
本论文较全面地综述了机场容量和延误评估问题的历史发展和最新研究成果，论述了研究中的关键问题。提出了天气影响的机场容量和延误评估相关概念，分别剖析了天气影响的容量和延误评估过程。分析了天气与容量之间的关系，从天气季节性、天气类型和天气预测三个方面出发，对天气影响的容量评估问题展开深入研究。针对天气与延误之间的关系，建立延误模型，并从比较延误的离散性能基准和连续性能基准两个角度出发，对天气影响的机场延误评估问题进行深入研究。
分析了机场天气、延误、需求和容量等概念，阐述了各概念之间的相互关系。针对某具体机场，分析了天气对容量和延误的影响，并给出实例分析，为天气影响的机场容量和延误评估奠定基础。
针对天气季节性影响的容量评估，建立了基于时间序列的季节划分模型。以天气影响的容量概率分布来表征天气特征，设计遗传算法将基于容量概率分布的时间序列中具有相似天气特征的月识别为一个季。在此基础上，将各季天气转换为容量概率分布。最后，以具体机场为例进行算例分析，结果表明根据容量概率分布得到的季与实际天气季节是一致的。
针对天气类型影响的容量评估，分析了恶劣天气发生的影响因素，提出了决策树和神经网络相结合的天气分类与识别方法，用于自动识别历史天气数据集中每一恶劣天气事件所属的天气类型。在此基础上，将每种天气类型转换为容量概率分布。最后以具体机场为例进行算例分析。
基于天气预测的容量预测，针对天气预测的实时、动态性特点，建立基于预测天气的预测容量模型。考虑到预测容量是在预测的多种天气类型背景下发生的，以预测天气数据和每种天气类型的容量概率分布作为输入，运用全概公式将概率预测天气转换为概率预测容量。最后根据机场某日天气预测数据，对该日预测容量分布进行了算例分析。
基于离散性能基准的天气影响延误评估，通过将机场到达建模为瞬时排队模型，研究机场典型天气对应的延误模式，并建立评估延误的离散性能基准，对天气影响的延误进行评估；最后，针对某日天气状况，对该日天气影响的延误分布走势进行评估分析。
基于连续性能基准的天气影响延误评估，针对机场延误与天气和交通需求之间具有的线性关系，建立单一模糊线性回归模型；考虑到延误与天气和需求之间同时还存在非线性关系，建立分段模糊线性回归模型。求解模型得到估计延误，通过比较估计延误与实际延误，建立评估延误的连续性能基准，对天气影响的延误进行评估。最后，针对某日天气状况，对该日天气影响的延误进行评估分析。
基于天气不确定的概率交通流量管理已逐渐成为国内外研究的热点。本文对天气影响的容量和延误评估问题进行了比较全面和系统的研究，提出的相关模型和方法针对性强且易于实现，弥补了我国在该领域研究基础薄弱的不足，不仅完善了天气影响的容量与延误评估理论体系，而且为今后概率流量管理在我国的研究与应用作了必要的理论准备。
Severe weather has been identified as the most important causal factor for causing airportcapacity reduction. Because the weather is complicated and changeable, the impacts of weather oncapacity have significant randomness, real-time and the dynamic characteristic. In view of this, themodels in majority research were built not including the weather influence factors. Therefore, airportcapacity assessment effected by weather is the urgent issues to be solved. With the steady rise indemand for air transportation, capacity and demand imbalances caused by severe weather becomemore prominent as the most important causal factor for flight delays. Therefore, to guide flow controldecisions during the day of operations, and for post operations analysis, it is useful to establish amodel that characterizes the relation between weather and delays and create a baseline for delayestimation, so as to assess delays under similar weather and traffic demand influences.
In this dissertation, the history development and the latest research results of airport capacity anddelay assessment issues were summarized, and the key problems to be solved were discussed. Theconcepts of airport capacity and delay assessment affected by weather were presented, and theevaluation process of them was analyzed respectively. The relation between weather and capacity wasdiscussed, and airport capacity assessments affected by weather were researched for weather seasonalcharacter, weather types and weather forecasts respectively. For the relation between weather anddelay，the delay model were developed, and airport delay assessment affected by weather wereresearched for discrete and continuous baseline respectively.
The concepts of airport weather, delay, demand and capacity were analyzed, and the relationbetween of them were discussed. For the specific airport, the impact of weather on capacity and delaywas analyzed, and numerical example was presented.
For capacity assessment affected by weather seasonal changes, season division model based timeseries was built。Capacity probabilistic distribution of historical weather was acquired to characterizeweather features, and a generic algorithm was designed to recognize several months of similarweather characteristics as a season. On this basis, according to the capacity distribution modelaffected by weather, capacity probabilistic distribution of historical weather in each season wasacquired. Finally, numerical simulation and analysis were carried out for the pacific airport, and theresults indicate that the seasons based on the capacity probabilistic distribution are in accordance withthe actual seasonal weather effects.
For capacity assessment affected by weather types, the influence factors of severe weather areanalyzed, and the methodology of weather type identification with combination of decision tree andneural network was proposed, which used in the automatic identification of weather type to whicheach severe weather event belongs. On this basis, according to the capacity distribution modelaffected by weather, capacity probabilistic distribution of each weather type was acquired. Finally,numerical simulation and analysis were carried out for the pacific airport.
For capacity forecasts based on weather forecasts，aiming at real-time and dynamic characteristicsof weather forecasts, the forecasts model of capacity distribution based on weather forecasts was built.Considering that capacity forecasts occur in the context of a variety of weather types, we translateprobabilistic weather forecasts into probabilistic capacity forecasts using full probability formula,which require weather forecast data and capacity probabilistic distribution of each weather type asinputs. Finally, numerical simulation and analysis were carried out for predicting capacityprobabilistic distribution on a particular day, according to weather forecast on the day.
For delay assessment affected by weather based on discrete baseline, typical delay pattern modescorresponding typical weather pattern modes were studied through development of a delay estimationmodel using instantaneous queuing model, and discrete baselines was created to measure theoperational delay affected by weather. Finally, according to the weather conditions in a day, the trendof delay distribution affected by weather on the day was analyzed.
For delay assessment affected by weather based on continuous baseline, a single fuzzy linearregression model was built for the linear relationship between traffic demand and weather. Taking intoaccount that the nonlinear relationship also exists between traffic demand and weather, a piece-wisefuzzy linear regression model was built. The model was solved for delay estimation, and a continuousbaselines was created to measure the operational delay affected by weather. Finally, according to theweather conditions in a day, the average delay for the day affected by weather was analyzed.
Probabilistic traffic flow management considering weather is becoming a research hotspot. Thedissertation is a deep research on capacity and delay assessment affected by weather. The proposedrelated models and methods have strong pertinence and are easy to be realized. The research of thispaper is conducive to improve our country’s study level in this field, and provide the theorypreparation for the further research and application of probabilistic traffic flow management.
本论文较全面地综述了机场容量和延误评估问题的历史发展和最新研究成果，论述了研究中的关键问题。提出了天气影响的机场容量和延误评估相关概念，分别剖析了天气影响的容量和延误评估过程。分析了天气与容量之间的关系，从天气季节性、天气类型和天气预测三个方面出发，对天气影响的容量评估问题展开深入研究。针对天气与延误之间的关系，建立延误模型，并从比较延误的离散性能基准和连续性能基准两个角度出发，对天气影响的机场延误评估问题进行深入研究。
分析了机场天气、延误、需求和容量等概念，阐述了各概念之间的相互关系。针对某具体机场，分析了天气对容量和延误的影响，并给出实例分析，为天气影响的机场容量和延误评估奠定基础。
针对天气季节性影响的容量评估，建立了基于时间序列的季节划分模型。以天气影响的容量概率分布来表征天气特征，设计遗传算法将基于容量概率分布的时间序列中具有相似天气特征的月识别为一个季。在此基础上，将各季天气转换为容量概率分布。最后，以具体机场为例进行算例分析，结果表明根据容量概率分布得到的季与实际天气季节是一致的。
针对天气类型影响的容量评估，分析了恶劣天气发生的影响因素，提出了决策树和神经网络相结合的天气分类与识别方法，用于自动识别历史天气数据集中每一恶劣天气事件所属的天气类型。在此基础上，将每种天气类型转换为容量概率分布。最后以具体机场为例进行算例分析。
基于天气预测的容量预测，针对天气预测的实时、动态性特点，建立基于预测天气的预测容量模型。考虑到预测容量是在预测的多种天气类型背景下发生的，以预测天气数据和每种天气类型的容量概率分布作为输入，运用全概公式将概率预测天气转换为概率预测容量。最后根据机场某日天气预测数据，对该日预测容量分布进行了算例分析。
基于离散性能基准的天气影响延误评估，通过将机场到达建模为瞬时排队模型，研究机场典型天气对应的延误模式，并建立评估延误的离散性能基准，对天气影响的延误进行评估；最后，针对某日天气状况，对该日天气影响的延误分布走势进行评估分析。
基于连续性能基准的天气影响延误评估，针对机场延误与天气和交通需求之间具有的线性关系，建立单一模糊线性回归模型；考虑到延误与天气和需求之间同时还存在非线性关系，建立分段模糊线性回归模型。求解模型得到估计延误，通过比较估计延误与实际延误，建立评估延误的连续性能基准，对天气影响的延误进行评估。最后，针对某日天气状况，对该日天气影响的延误进行评估分析。
基于天气不确定的概率交通流量管理已逐渐成为国内外研究的热点。本文对天气影响的容量和延误评估问题进行了比较全面和系统的研究，提出的相关模型和方法针对性强且易于实现，弥补了我国在该领域研究基础薄弱的不足，不仅完善了天气影响的容量与延误评估理论体系，而且为今后概率流量管理在我国的研究与应用作了必要的理论准备。
Severe weather has been identified as the most important causal factor for causing airportcapacity reduction. Because the weather is complicated and changeable, the impacts of weather oncapacity have significant randomness, real-time and the dynamic characteristic. In view of this, themodels in majority research were built not including the weather influence factors. Therefore, airportcapacity assessment effected by weather is the urgent issues to be solved. With the steady rise indemand for air transportation, capacity and demand imbalances caused by severe weather becomemore prominent as the most important causal factor for flight delays. Therefore, to guide flow controldecisions during the day of operations, and for post operations analysis, it is useful to establish amodel that characterizes the relation between weather and delays and create a baseline for delayestimation, so as to assess delays under similar weather and traffic demand influences.
In this dissertation, the history development and the latest research results of airport capacity anddelay assessment issues were summarized, and the key problems to be solved were discussed. Theconcepts of airport capacity and delay assessment affected by weather were presented, and theevaluation process of them was analyzed respectively. The relation between weather and capacity wasdiscussed, and airport capacity assessments affected by weather were researched for weather seasonalcharacter, weather types and weather forecasts respectively. For the relation between weather anddelay，the delay model were developed, and airport delay assessment affected by weather wereresearched for discrete and continuous baseline respectively.
The concepts of airport weather, delay, demand and capacity were analyzed, and the relationbetween of them were discussed. For the specific airport, the impact of weather on capacity and delaywas analyzed, and numerical example was presented.
For capacity assessment affected by weather seasonal changes, season division model based timeseries was built。Capacity probabilistic distribution of historical weather was acquired to characterizeweather features, and a generic algorithm was designed to recognize several months of similarweather characteristics as a season. On this basis, according to the capacity distribution modelaffected by weather, capacity probabilistic distribution of historical weather in each season wasacquired. Finally, numerical simulation and analysis were carried out for the pacific airport, and theresults indicate that the seasons based on the capacity probabilistic distribution are in accordance withthe actual seasonal weather effects.
For capacity assessment affected by weather types, the influence factors of severe weather areanalyzed, and the methodology of weather type identification with combination of decision tree andneural network was proposed, which used in the automatic identification of weather type to whicheach severe weather event belongs. On this basis, according to the capacity distribution modelaffected by weather, capacity probabilistic distribution of each weather type was acquired. Finally,numerical simulation and analysis were carried out for the pacific airport.
For capacity forecasts based on weather forecasts，aiming at real-time and dynamic characteristicsof weather forecasts, the forecasts model of capacity distribution based on weather forecasts was built.Considering that capacity forecasts occur in the context of a variety of weather types, we translateprobabilistic weather forecasts into probabilistic capacity forecasts using full probability formula,which require weather forecast data and capacity probabilistic distribution of each weather type asinputs. Finally, numerical simulation and analysis were carried out for predicting capacityprobabilistic distribution on a particular day, according to weather forecast on the day.
For delay assessment affected by weather based on discrete baseline, typical delay pattern modescorresponding typical weather pattern modes were studied through development of a delay estimationmodel using instantaneous queuing model, and discrete baselines was created to measure theoperational delay affected by weather. Finally, according to the weather conditions in a day, the trendof delay distribution affected by weather on the day was analyzed.
For delay assessment affected by weather based on continuous baseline, a single fuzzy linearregression model was built for the linear relationship between traffic demand and weather. Taking intoaccount that the nonlinear relationship also exists between traffic demand and weather, a piece-wisefuzzy linear regression model was built. The model was solved for delay estimation, and a continuousbaselines was created to measure the operational delay affected by weather. Finally, according to theweather conditions in a day, the average delay for the day affected by weather was analyzed.
Probabilistic traffic flow management considering weather is becoming a research hotspot. Thedissertation is a deep research on capacity and delay assessment affected by weather. The proposedrelated models and methods have strong pertinence and are easy to be realized. The research of thispaper is conducive to improve our country’s study level in this field, and provide the theorypreparation for the further research and application of probabilistic traffic flow management.
引文
[1] Abdelghany K F, Shah S S, Raina S, Abdelghany A F. A model for projecting flight delaysduring irregular operation conditions[J]. Journal of Air Transport Management,2004,10:385–394.
[2] Bureau of Transportation Statistics(BTS). Understanding the reporting of causes of flightdelays and cancellations[R]. Washington, D.C: U.S. Department of Transportation, Office ofAirline Information,2007.
[3] Krozel J, Capozzi B, Andre A D, et al. The Future National Airspace System: DesignRequirements Imposed By Weather Constraints[C]. Proceedings of AIAA Guidance,Navigation, and Control Conference. Austin, TX,2003.
[4]中国民用航空总局规划发展司.从统计看民航2009[M].北京:中国民航出版社,2009.
[5] Clark D A. Investigating a New Ground Delay Program Strategy for Coping with SFOStratus[C].89th AMS Annual Meeting ARAM Special Symposium on Weather-Air Traffic.Phoenix, AZ,2009.
[6] Andreatta G, Romanin J G. Airport flow management under congestion [J]. TransportationScience,1987,21:249-253.
[7] Richetta O, Odoni A R. Solving optimally the static ground holding policy problem in airtraffic control[J]. Transportation Science,1993,24:228-238.
[8] Alexander Klein, Sadegh Kavoussi, Robert S L. Weather Forecast Accuracy: Study ofImpact on Airport Capacity and Estimation of Avoidable Costs[C]. Proceedings of the8thUSA/Europe Air Traffic Management R&D Seminars. Santa Fe, NM,2009:1-11.
[9]中国民用航空局.机场容量评估管理暂行办法[Z].2010.
[10] Hoffman R L. Integer programming models for ground-holding in air traffic flowmanagement [D]. College Park, Maryland: University of Maryland,1997.
[11] Neufville R D, Odoni A. Airport systems: planning, design, and management[M]. New York:McGraw-Hill,2003.
[12] Blumstein A. Airport capacity: representation, estima-tion, optimization[R]. CornellAeronautical LaboratoryReport,1960.
[13] Kim A, Hansen M. Validation of Runway Capacity Models[C].8th USA/Europe Air TrafficManagement Research and Development Seminar, Santa Fe, NM,2009.
[14] Horanjic B R. Some Queuing Models of Airport Delays[D]. Cambridge, MA: OperationsResearch Center, Massachusetts Institute of Technology,1990.
[15] Bowen E G, Pearcey T. Delays in the Flow of Air Traffic[J]. J.R. Aeronauties,1948,52:251-258.
[16] Bulmstein A.The landing capacity of a runway[J].Operation esearch,1959,7(1):752-763.
[17] Blumstein A. An analytical investigation of airport capacity[R]. Ithaca, NY: CornellAeronautical Laboratory,1960.
[18] Blumstein A. The Operations Capacity of a Runway used for Landings and Takeoffs[C].2ndInternational Conference on Operational Research.1961:657-672.116
[19] Amodeo F A, Haines A L, Sinha A N. Concepts for estimating capacity of basic runwayconfigurations[R].Virginia:The MITRE Corporation,1977.
[20] Hockaday S L M, Kanafani A. Developments in airport capacity analysis[J]. TransportationResearch,1974,8(1):171-180.
[21] Swedish W. Upgraded FAA airfield capacity model[R]. McLean,VA: The MITRECorporation,1981.
[22] Odoni A R, Bowman J, Delahaye D, et al. Existing and required modeling capabilities forevaluating ATM systems and concepts[R]. Cambridge, MA: MIT International Center for AirTransportation,1998.
[23] Gilbo E P. Optimizing airport capacity utilization in air traffic flow management subject toconstraints at arrival and departure fixes [J]. IEEE Transactions on Control SystemTechnology,1997,5(5):490-503.
[24] Hall W. Efficient Capacity Allocation in a Collaborative Air Transportation System[D].Lexington, MA: Massachusetts Institute of Technology,1999.
[25] Harris R.M. Models for Runway Capacity Analysis[R]. Washington D.C: The MitreCorporation,1969.
[26] Lee D A, Nelson C, Shapiro G. The aviation system analysis capability: airport capacity anddelay models[R]. Hampton, Virginia: National Aeronautics and Space Administration,Langley Research Center,1998.
[27] Lee D A, Kostiuk P F. LMI runway capacity model, MIT AATT Model Project[R].Cambridge, MA: Massachusetts Institute of Technology,1996.
[28][1] Zografos K G, Miltos A, Odoni A R. An Analytical Model for Runway SystemCapacity Analysis[A]. IFAC Transportation Systems[C]. Chania, Greece,1997.
[29]刘松.机场空中交通容量估计系统研究[D].南京:南京航空航天大学,1999.
[30]丁峰,贺尔铭,吴盘龙.空中交通自动化管理中飞机等待队列的排序算法[J].西北工业大学学报，2001,19(3):456-460.
[31]彭莉娟,吴鹍,余静.机场跑道最大容量评估模型的研究[J].四川大学学报(自然科学版),2006,43(5):1018-1022.
[32]王维,邓松武.跑道快速出口优化模型及仿真分析[J].中国民航学院学报,2006,24(4):27-31.
[33]余江,蒲云.跑道着陆容量的数学模型及其分析[J].西南交通大学学报,2004,39(1):42-26.
[34]卢朝阳.基于T系统模型的跑道容量评估新方法[J].南京航空航天大学学报,2010,42(1):43-46.
[35] Federal Aviation Administration, Technical Report on Airport Capacity and Delay Studies[R].Washington, D.C: FAA,1976.
[36] Newwell G F, Airport capacity and delays[J]. Transporation Science,1979,13(3):201-241.
[37] ATAC Corporation. SIMMOD PLUS Reference Manual[Z].2007.
[38]倪桂明,杨东援.机场系统计算机仿真研究的应用与发展[J].系统仿真学报,2002,14(1)：112-115.
[39]倪桂明.机场系统评价与计算机仿真分析研究[D].上海:同济大学,2000.
[40]蒋兵,胡明华.终端区空中交通容量评估的仿真方法[J].交通运输工程学报,2003,3(1):97-100.
[41]王晓敏,聂润兔.基于Monte-Carlo模拟方法的跑道容量估计[J].中国民航学院学报,2005,24(增刊):13-15.
[42]孙樊荣.航班计划容量的仿真评估方法及应用[D].南京:南京航空航天大学,2006.
[43]万莉莉,胡明华.管制员工作负荷及扇区容量评估问题研究.交通运输工程与信息学报[J].2006,4(2):70-75.
[44] Schmidt D K. A Queuing Analysis of the Air Traffic Controller’s workload. Transactions onsystems, man and cybernetics.1978,8(6):492-498.
[45] Reaux R A. Murphy E.D., Stewart, et al.. Building a modeling and simulation analysis toolto predict air traffic controller workload and performance[C]. Human Factors SocietyProceedings of the Human Factors Society33rd Annual Meeting Perspectives. california:Human Factors Society,1989:52-56.
[46] Tofukuji N, An Enroute ATC Simulation Experiment for Sector Capacity Estimation. IEEETransaction on Control Systems Technology[J].1993,1(3):138-143.
[47]裴成功,韩松臣,刘星.管制员工作负荷评估的回归分析法[J].南京航空航天大学学报,2007,39(1):107-112.
[48] Mueller E, Chatterji G B. Analysis of Aircraft Arrival and Departure DelayCharacteristics[C]. AIAA. Proceedings of AIAA Aircraft Technology, Integration, andOperations. Los Angeles, CA: AIAA,2002:1-14.
[49] Paker, E C, Rhody S E, Greenland A. Methodologies for Analyzing the Principal Factors thatAffect National Airspace System Performance[J]. Air Traffic Control Quarterly,10(3):197-221.
[50] Xu N, Donohue G, Laskey K, et al. Bayesian Network Analysis of Flight Delays[C].Transportation Research Board. TRB86th Annual Meeting Compendium of PapersCD-ROM. Washington D.C: Transportation Research Board,2007:1-12.
[51] Krozel J, Hoffman B, Penny S, et al. Aggregate Statistics of the National AirspaceSystem[C]. Proceedings of AIAA Guidance, Navigation, and Control Conference. Austin,TX,2003:1-15.
[52] Abdel-Aty M, Lee C, Bai Y, et al. Detecting periodic patterns of arrival delay[J]. Journal ofAir Transport Management,2007,13:355-361.
[53] Liu P B, Hansen M, Mukherjee A. Scenario-based air traffic flow management: From theoryto practice[J]. Transportation Research Part B,2008,42:685-702.
[54] Mukherjee A, Lovell D J, Ball M O, et al. Modeling Delays and Cancellation Probabilities toSupport Strategic Simulations[C]. FAA/EUROCONTROL. Proceedings of6th USA/EuropeAir Traffic Management R&D Seminar. Baltimore, MD,2005:1-10.
[55] Mukherjee A, Lovell D J, Ball M O, et al. Using Optimization and Queuing Models toEstimate Long-Term Average Flight Delays and Cancellation Rates[EB/OL][2009-08-16].http://transp-or2.epfl.ch/tristan/FullPapers/164Ball.pdf.
[56]徐涛,丁建立,顾彬等.基于增量式排列支持向量机的机场航班延误预警[J].航空学报,2009,30(7):1256-1262.
[57]袁瑗,陈兵,徐涛等.基于先验知识的多类CVM航班延误预警模型[J].吉林大学学报(工学版),2010,40(3):752-757.
[58] Abundo S. An approach for estimating delays at a busy airport[D]. Cambridge, MA:Operations Research Center, Massachusetts Institute of Technology,1990.
[59] Inniss T R, Ball M O. Estimating One-Parameter Airport Arrival Capacity Distributions forAir Traffic Flow Management[R], Washington, D.C: NEXTOR,2002.
[60] Inniss T R, Ball M O. Stochastic Models for the Estimation of Airport Arrival CapacityDistributions[D]. College Park, Maryland: University of Maryland,2000.
[61] Yen J W, Zabinsky Z B, Grignon L. Incorporating Weather Uncertainty in Airport ArrivalRate Decisions[Z]. FAA-NEXTOR-INFORMS. Conference on Air Traffic Management andControl.2003.
[62] Allan S S, Beesley J A, Evans J E, et al. Gaddy Analysis of Delay Causality at NewarkInternational Airport[C].4th USA/Europe Air Traffic Management R&D Seminar, Santa Fe,New Mexico,2001.
[63] Hoffman B, Krozel J, Roy A, et al. A Cluster Analysis to Classify Days in the NationalAirspace System[C]. Proceedings of AIAA Guidance, Navigation, and Control Conference.Austin, TX,2003.
[64] Krozel J, Hoffman B, Penny S, et al. Selection of Datasets for NAS-Wide SimulationValidations[R]. Herndon: Metron Aviation,2002.
[65] Penny S, Hoffman B, Krozel J A, et al. Classification of Days in the National AirspaceSystem Using Cluster Analysis[J]. Air Traffic Control Quarterly,2005,13(1):29-54.
[66] Callaham M B, DeArmon J S, Cooper A M, et al. Assessing NAS Performance: Normalizingfor the Effects of Weather[C]. FAA/EUROCONTROL. Proceedings of the4th USA/EuropeAir Traffic Management R&D Seminars. Santa Fe, NM,2001:1-11.
[67] Chatterji G, Sridhar B. National Airspace System Delay Estimation Using Weather WeightedTraffic Counts[C]. AIAA Guidance, Navigation and Control Conference. San Francisco,2005:1-17.
[68] Sridhar B, Swei S. Relationship between Weather, Traffic and Delay Based on EmpiricalMethods[C].6th AIAA Aviation Technology, Integration and Operations Conference (ATIO).Wichita, KS,2006.
[69] Sridhar B, Swei S. Classification and Computation of Aggregate Delay Using Center-BasedWeather Impacted Traffic Index[C].7th AIAA Aviation Technology, Integration andOperations Conference (ATIO). Belfast, Northern Ireland,2007.
[70] Klein A, Jehlen R, Liang D. Weather Index With Queuing Component For National AirspaceSystem Performance Assessment[C].7th USA-Europe ATM R&D Seminar. Barcelona,Spain,2007.
[71] Klein A, Kavoussi S, Hickman D, et at. Predicting Weather Impact on Air Traffic[C]. ICNSConference. Herndon, VA,2007.
[72] Hansen M, Xiong J. Weather Normalization for Evaluating National Airspace System (NAS)Performance[C].7th USA/Europe ATM R&D Seminar. Barcelona, Spain,2007.
[73] Sridhar, B. and Chen, N. Short Term National Airspace System Delay Prediction UsingWeather Impacted Traffic Index[C]. AIAA Guidance, Navigation and Control Conference,Honolulu, HI,2008.
[74] Milan Janic. Modeling Airport Operations Affected by a Large-Scale Disruption[J].JOURNAL OF TRANSPORTATION ENGINEERING,2009,135(4):206-216.
[75] Enhanced Traffic Management System(ETMS): Reference Manual[R]. U.S. Department ofTransportation VNTSC-DTS56-TMS-004,2004.
[76] Air traffic flow and capacity management operations: ATFCM users manual [S].2006.
[77]张通国,詹建民,朱衍波等.日本空中交通流量管理系统考察报告[R].2004
[78]章澄昌.飞行气象学[M].北京:气象出版社,2000.
[79] Hocker G A. Airport Demand and Capacity Modeling for Flow Management Analysis[D].Lexington, MA: Massachusetts Institute of Technology,1994.
[80]刘得一.民航概论[M].北京:中国民航出版社,2000.
[81]陈廷良.现代运输机航空气象学[M].北京:气象出版社,1992.
[82] Yu J S. Airport Capacity and Regional Weather Modeling [D]. Lexington, MA:Massachusetts Institute of Technology,1994.
[83] Krozel Jimmy, William M. McNichols, et al. Causality Analysis for Aviation WeatherHazards[C]. The26th Congress of International Council of the Aeronautical Sciences(ICAS), Anchorage, Alaska,2008.
[84]赵树海.航空气象学[M].北京:气象出版社,1994.
[85] Keller J L. Data Requirements for Ceiling and Visibility Products Development[R].Lexington: Lincoln Laboratory, Massachusetts Institute of Technology,1994.
[86] Bergot T, Guedalia D. Numerical forecasting of radiation fog. Part I: numerical model andsensitivity tests[J]. Monthly Weather Review,1994:1218-1230.
[87] Tardif R. Characterizing Fog Occurrences In the Northeasteran United States UsingHistorical Data[C].11th Conference on Aviation, Range and Aerospace Meteorology,Hyannis MA,2004
[88] Hansen M, Winbin W. Empirical Analysis of Airport Capacity Enhancement Impacts: A CaseStudy of DFW Airport[R]. NEXTOR Research Report UCB-ITS-RR-1998-17,1998.
[89] FRON X. Performance indicators, targets, steering[C]. Technical Interchange meeting,Toulouse,2002.
[90] U.S. Department of Transportation, FAA. ORDER JO7210.55D[Z].2007.
[91] Hansen M, Post-deployment analysis of capacity and delay impacts of an airportenhancement: case of a new runway at Detroit[J]. Air Traffic Control Quarterly,2004,12(4):339-365.
[92] International Civil Aeronautics Organization. Air Traffic Management[Z].2007.
[93] Wells A T, Young S B. Airport planning and management[M]. New York: McGraw-Hill,2004.
[94] Ashford N, Wright P H. Airport engineering[M]. New York: A Wiley-IntersciencePublication.1984.
[95]余江.机场扩展终端区的运行优化策略研究[D].成都:西南交通大学,2005.
[96] Raguraman K. Getting Planes Off the Ground: Key Concepts and Issues in Airport CapacityPlanning and Management[R]. Institute of Transport Studies, The Australian Key Center inTransport Management, The University of Sydney and Monash University,1999.
[97] Schaefer L, Millner D. Flight delay propagation analysis with the detailed policy assessmenttool[C]. Proceedings of the Annual IEEE Systems, Man, and Cybernetics Conference,Tucson,2001.
[98] Subramanian P. A Simulation Study To Investigate Runway Capacity Using TAAM[D].Daytona Beach, Florida: Embry-Riddle Aeronautical University,2002.
[99] Sorensen J, Hunter G, Shen M. Requirements for Integrated Flight and Traffic ManagementDuring Final Approach[C]. IEEE Position Location and Navigation Symposium, Monterey,CA,1992.
[100] Glenn H P, Timothy J Dasey. A Statistical Analysis of Approach Winds atCapacity-Restricted Airports [C]. Proceedings of the Digital Avionics Systems Conference(DASC00), Philadelphia, PA,2004.
[101] Global Integrated Surface Hourly data [EB/OL].http://cdo.ncdc.noaa.gov/cgi-bin/cdo/cdostnsearch.pl
[102] Global Surface Summary of Day Data[EB/OL].http://www.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html
[103] Clark D A, Wilson F W. The Marine Stratus Initiative at San Francisco InternationalAirport[R]. Lexington, MA: Massachusetts Institute of Technology,1996.
[104] Evans J E, Dasey T J, Rhoda DA. Weather Sensing and Data Fusion to Improve Safety andReduce Delays at Major West Coast Airports[R]. Lexington: Lincoln Laboratory,Massachusetts Institute of Technology,1999.
[105] Marzban C, Leyton S, Colman B. Ceiling and Visibility Forecasts via Neural Networks[J].WEATHER AND FORECASTING,2007,22:466-479.
[106] FAA. Airport Benchmark Capacity Report[R]. Washington, D.C: U.S. Department ofTransportation,2004.
[107] Hilliker J L, Fritsch J M. An observations-based statistical system for warm-season hourlyprobabilistic forecasts of low ceiling at the San Francisco International Airport[J]. Appl.Meteor.,1999,38:1692–1705.
[108] Leipper D F. Fog on the U.S. West Coast: A review[J]. Bulletin of the AmericanMeteorological Society,1994,75:229–240.
[109] The Global ATM operational concept[C].11th Air Navigation Conference, Montreal,2003.
[110] Dlugi O, Asheimer T, Baldoni C, et al. The ATM Target Concept D3[R]. SESAR consortium,2007.
[111] Andrew F L, Sean M, Mahoney J. A Lead-time Metric For Assessing Skill in ForecastingThe Onset of IFR Events[C].13th Conference on Aviation, Range, and AerospaceMeteorology, New Orleans, LA,2008.
[112]盛骤,谢式千,潘承毅.概率论与数理统计[M].北京:高等教育出版社,2007.
[113]杜勇宏.季节时间序列理论与应用[M].天津:南开大学出版社,2008.
[114]董晓莉.时间序列数据挖掘相似性度量和周期模式挖掘研究[D].天津:天津大学,2007.
[115]冯红伟.数据挖掘技术的研究及应用[D].西安:西北工业大学,2002.
[116]董云影.基于遗传算法的模糊聚类技术的研究[D].大连:大连海事大学,2005.
[117]袁方,周志勇,宋鑫.初始聚类中心优化的k-means算法[J].计算机工程,2007,33(3):65-66.
[118] DUDA R O.模式分类[M].李宏东,姚天翔译.北京:机械工业出版社,2003.
[119] Clark DA. Characterizing the causes of low ceiling and visibility at U.S. airports[C].//6thConference on Aviation Weather Systems. Dallas, TX,1995:325-330.
[120] Tom M M. Machine Learning[M]. New York: McGraw-Hill,1997.
[121]杨行峻,郑君里.人工神经网络[M].北京:高等教育出版社,1992.
[122]刘棋.决策树ID3算法的改进研究[D].哈尔滨:哈尔滨工程大学,2009.
[123] Haykin S.神经网络原理[M].叶世伟,史忠植译.北京:机械工业出版社,2004.
[124]何述东等.多层前向神经网络结构的研究进展[J].控制理论与应用,1998,15(3):59-66.
[125] Ramamoorthy K, Hunter G. Modeling and Performance of NAS in Inclement Weather[C].6th AIAA Aviation, Integration, and Operations Conference. Wichita, KS,2006:25-27.
[126]张军,葛军,田俊杰等.概率天气预报及其应用[M].北京:气象出版社,1998.
[127]陆传赉.排队论[M].北京:北京邮电大学出版社,2009.
[2] Bureau of Transportation Statistics(BTS). Understanding the reporting of causes of flightdelays and cancellations[R]. Washington, D.C: U.S. Department of Transportation, Office ofAirline Information,2007.
[3] Krozel J, Capozzi B, Andre A D, et al. The Future National Airspace System: DesignRequirements Imposed By Weather Constraints[C]. Proceedings of AIAA Guidance,Navigation, and Control Conference. Austin, TX,2003.
[4]中国民用航空总局规划发展司.从统计看民航2009[M].北京:中国民航出版社,2009.
[5] Clark D A. Investigating a New Ground Delay Program Strategy for Coping with SFOStratus[C].89th AMS Annual Meeting ARAM Special Symposium on Weather-Air Traffic.Phoenix, AZ,2009.
[6] Andreatta G, Romanin J G. Airport flow management under congestion [J]. TransportationScience,1987,21:249-253.
[7] Richetta O, Odoni A R. Solving optimally the static ground holding policy problem in airtraffic control[J]. Transportation Science,1993,24:228-238.
[8] Alexander Klein, Sadegh Kavoussi, Robert S L. Weather Forecast Accuracy: Study ofImpact on Airport Capacity and Estimation of Avoidable Costs[C]. Proceedings of the8thUSA/Europe Air Traffic Management R&D Seminars. Santa Fe, NM,2009:1-11.
[9]中国民用航空局.机场容量评估管理暂行办法[Z].2010.
[10] Hoffman R L. Integer programming models for ground-holding in air traffic flowmanagement [D]. College Park, Maryland: University of Maryland,1997.
[11] Neufville R D, Odoni A. Airport systems: planning, design, and management[M]. New York:McGraw-Hill,2003.
[12] Blumstein A. Airport capacity: representation, estima-tion, optimization[R]. CornellAeronautical LaboratoryReport,1960.
[13] Kim A, Hansen M. Validation of Runway Capacity Models[C].8th USA/Europe Air TrafficManagement Research and Development Seminar, Santa Fe, NM,2009.
[14] Horanjic B R. Some Queuing Models of Airport Delays[D]. Cambridge, MA: OperationsResearch Center, Massachusetts Institute of Technology,1990.
[15] Bowen E G, Pearcey T. Delays in the Flow of Air Traffic[J]. J.R. Aeronauties,1948,52:251-258.
[16] Bulmstein A.The landing capacity of a runway[J].Operation esearch,1959,7(1):752-763.
[17] Blumstein A. An analytical investigation of airport capacity[R]. Ithaca, NY: CornellAeronautical Laboratory,1960.
[18] Blumstein A. The Operations Capacity of a Runway used for Landings and Takeoffs[C].2ndInternational Conference on Operational Research.1961:657-672.116
[19] Amodeo F A, Haines A L, Sinha A N. Concepts for estimating capacity of basic runwayconfigurations[R].Virginia:The MITRE Corporation,1977.
[20] Hockaday S L M, Kanafani A. Developments in airport capacity analysis[J]. TransportationResearch,1974,8(1):171-180.
[21] Swedish W. Upgraded FAA airfield capacity model[R]. McLean,VA: The MITRECorporation,1981.
[22] Odoni A R, Bowman J, Delahaye D, et al. Existing and required modeling capabilities forevaluating ATM systems and concepts[R]. Cambridge, MA: MIT International Center for AirTransportation,1998.
[23] Gilbo E P. Optimizing airport capacity utilization in air traffic flow management subject toconstraints at arrival and departure fixes [J]. IEEE Transactions on Control SystemTechnology,1997,5(5):490-503.
[24] Hall W. Efficient Capacity Allocation in a Collaborative Air Transportation System[D].Lexington, MA: Massachusetts Institute of Technology,1999.
[25] Harris R.M. Models for Runway Capacity Analysis[R]. Washington D.C: The MitreCorporation,1969.
[26] Lee D A, Nelson C, Shapiro G. The aviation system analysis capability: airport capacity anddelay models[R]. Hampton, Virginia: National Aeronautics and Space Administration,Langley Research Center,1998.
[27] Lee D A, Kostiuk P F. LMI runway capacity model, MIT AATT Model Project[R].Cambridge, MA: Massachusetts Institute of Technology,1996.
[28][1] Zografos K G, Miltos A, Odoni A R. An Analytical Model for Runway SystemCapacity Analysis[A]. IFAC Transportation Systems[C]. Chania, Greece,1997.
[29]刘松.机场空中交通容量估计系统研究[D].南京:南京航空航天大学,1999.
[30]丁峰,贺尔铭,吴盘龙.空中交通自动化管理中飞机等待队列的排序算法[J].西北工业大学学报，2001,19(3):456-460.
[31]彭莉娟,吴鹍,余静.机场跑道最大容量评估模型的研究[J].四川大学学报(自然科学版),2006,43(5):1018-1022.
[32]王维,邓松武.跑道快速出口优化模型及仿真分析[J].中国民航学院学报,2006,24(4):27-31.
[33]余江,蒲云.跑道着陆容量的数学模型及其分析[J].西南交通大学学报,2004,39(1):42-26.
[34]卢朝阳.基于T系统模型的跑道容量评估新方法[J].南京航空航天大学学报,2010,42(1):43-46.
[35] Federal Aviation Administration, Technical Report on Airport Capacity and Delay Studies[R].Washington, D.C: FAA,1976.
[36] Newwell G F, Airport capacity and delays[J]. Transporation Science,1979,13(3):201-241.
[37] ATAC Corporation. SIMMOD PLUS Reference Manual[Z].2007.
[38]倪桂明,杨东援.机场系统计算机仿真研究的应用与发展[J].系统仿真学报,2002,14(1)：112-115.
[39]倪桂明.机场系统评价与计算机仿真分析研究[D].上海:同济大学,2000.
[40]蒋兵,胡明华.终端区空中交通容量评估的仿真方法[J].交通运输工程学报,2003,3(1):97-100.
[41]王晓敏,聂润兔.基于Monte-Carlo模拟方法的跑道容量估计[J].中国民航学院学报,2005,24(增刊):13-15.
[42]孙樊荣.航班计划容量的仿真评估方法及应用[D].南京:南京航空航天大学,2006.
[43]万莉莉,胡明华.管制员工作负荷及扇区容量评估问题研究.交通运输工程与信息学报[J].2006,4(2):70-75.
[44] Schmidt D K. A Queuing Analysis of the Air Traffic Controller’s workload. Transactions onsystems, man and cybernetics.1978,8(6):492-498.
[45] Reaux R A. Murphy E.D., Stewart, et al.. Building a modeling and simulation analysis toolto predict air traffic controller workload and performance[C]. Human Factors SocietyProceedings of the Human Factors Society33rd Annual Meeting Perspectives. california:Human Factors Society,1989:52-56.
[46] Tofukuji N, An Enroute ATC Simulation Experiment for Sector Capacity Estimation. IEEETransaction on Control Systems Technology[J].1993,1(3):138-143.
[47]裴成功,韩松臣,刘星.管制员工作负荷评估的回归分析法[J].南京航空航天大学学报,2007,39(1):107-112.
[48] Mueller E, Chatterji G B. Analysis of Aircraft Arrival and Departure DelayCharacteristics[C]. AIAA. Proceedings of AIAA Aircraft Technology, Integration, andOperations. Los Angeles, CA: AIAA,2002:1-14.
[49] Paker, E C, Rhody S E, Greenland A. Methodologies for Analyzing the Principal Factors thatAffect National Airspace System Performance[J]. Air Traffic Control Quarterly,10(3):197-221.
[50] Xu N, Donohue G, Laskey K, et al. Bayesian Network Analysis of Flight Delays[C].Transportation Research Board. TRB86th Annual Meeting Compendium of PapersCD-ROM. Washington D.C: Transportation Research Board,2007:1-12.
[51] Krozel J, Hoffman B, Penny S, et al. Aggregate Statistics of the National AirspaceSystem[C]. Proceedings of AIAA Guidance, Navigation, and Control Conference. Austin,TX,2003:1-15.
[52] Abdel-Aty M, Lee C, Bai Y, et al. Detecting periodic patterns of arrival delay[J]. Journal ofAir Transport Management,2007,13:355-361.
[53] Liu P B, Hansen M, Mukherjee A. Scenario-based air traffic flow management: From theoryto practice[J]. Transportation Research Part B,2008,42:685-702.
[54] Mukherjee A, Lovell D J, Ball M O, et al. Modeling Delays and Cancellation Probabilities toSupport Strategic Simulations[C]. FAA/EUROCONTROL. Proceedings of6th USA/EuropeAir Traffic Management R&D Seminar. Baltimore, MD,2005:1-10.
[55] Mukherjee A, Lovell D J, Ball M O, et al. Using Optimization and Queuing Models toEstimate Long-Term Average Flight Delays and Cancellation Rates[EB/OL][2009-08-16].http://transp-or2.epfl.ch/tristan/FullPapers/164Ball.pdf.
[56]徐涛,丁建立,顾彬等.基于增量式排列支持向量机的机场航班延误预警[J].航空学报,2009,30(7):1256-1262.
[57]袁瑗,陈兵,徐涛等.基于先验知识的多类CVM航班延误预警模型[J].吉林大学学报(工学版),2010,40(3):752-757.
[58] Abundo S. An approach for estimating delays at a busy airport[D]. Cambridge, MA:Operations Research Center, Massachusetts Institute of Technology,1990.
[59] Inniss T R, Ball M O. Estimating One-Parameter Airport Arrival Capacity Distributions forAir Traffic Flow Management[R], Washington, D.C: NEXTOR,2002.
[60] Inniss T R, Ball M O. Stochastic Models for the Estimation of Airport Arrival CapacityDistributions[D]. College Park, Maryland: University of Maryland,2000.
[61] Yen J W, Zabinsky Z B, Grignon L. Incorporating Weather Uncertainty in Airport ArrivalRate Decisions[Z]. FAA-NEXTOR-INFORMS. Conference on Air Traffic Management andControl.2003.
[62] Allan S S, Beesley J A, Evans J E, et al. Gaddy Analysis of Delay Causality at NewarkInternational Airport[C].4th USA/Europe Air Traffic Management R&D Seminar, Santa Fe,New Mexico,2001.
[63] Hoffman B, Krozel J, Roy A, et al. A Cluster Analysis to Classify Days in the NationalAirspace System[C]. Proceedings of AIAA Guidance, Navigation, and Control Conference.Austin, TX,2003.
[64] Krozel J, Hoffman B, Penny S, et al. Selection of Datasets for NAS-Wide SimulationValidations[R]. Herndon: Metron Aviation,2002.
[65] Penny S, Hoffman B, Krozel J A, et al. Classification of Days in the National AirspaceSystem Using Cluster Analysis[J]. Air Traffic Control Quarterly,2005,13(1):29-54.
[66] Callaham M B, DeArmon J S, Cooper A M, et al. Assessing NAS Performance: Normalizingfor the Effects of Weather[C]. FAA/EUROCONTROL. Proceedings of the4th USA/EuropeAir Traffic Management R&D Seminars. Santa Fe, NM,2001:1-11.
[67] Chatterji G, Sridhar B. National Airspace System Delay Estimation Using Weather WeightedTraffic Counts[C]. AIAA Guidance, Navigation and Control Conference. San Francisco,2005:1-17.
[68] Sridhar B, Swei S. Relationship between Weather, Traffic and Delay Based on EmpiricalMethods[C].6th AIAA Aviation Technology, Integration and Operations Conference (ATIO).Wichita, KS,2006.
[69] Sridhar B, Swei S. Classification and Computation of Aggregate Delay Using Center-BasedWeather Impacted Traffic Index[C].7th AIAA Aviation Technology, Integration andOperations Conference (ATIO). Belfast, Northern Ireland,2007.
[70] Klein A, Jehlen R, Liang D. Weather Index With Queuing Component For National AirspaceSystem Performance Assessment[C].7th USA-Europe ATM R&D Seminar. Barcelona,Spain,2007.
[71] Klein A, Kavoussi S, Hickman D, et at. Predicting Weather Impact on Air Traffic[C]. ICNSConference. Herndon, VA,2007.
[72] Hansen M, Xiong J. Weather Normalization for Evaluating National Airspace System (NAS)Performance[C].7th USA/Europe ATM R&D Seminar. Barcelona, Spain,2007.
[73] Sridhar, B. and Chen, N. Short Term National Airspace System Delay Prediction UsingWeather Impacted Traffic Index[C]. AIAA Guidance, Navigation and Control Conference,Honolulu, HI,2008.
[74] Milan Janic. Modeling Airport Operations Affected by a Large-Scale Disruption[J].JOURNAL OF TRANSPORTATION ENGINEERING,2009,135(4):206-216.
[75] Enhanced Traffic Management System(ETMS): Reference Manual[R]. U.S. Department ofTransportation VNTSC-DTS56-TMS-004,2004.
[76] Air traffic flow and capacity management operations: ATFCM users manual [S].2006.
[77]张通国,詹建民,朱衍波等.日本空中交通流量管理系统考察报告[R].2004
[78]章澄昌.飞行气象学[M].北京:气象出版社,2000.
[79] Hocker G A. Airport Demand and Capacity Modeling for Flow Management Analysis[D].Lexington, MA: Massachusetts Institute of Technology,1994.
[80]刘得一.民航概论[M].北京:中国民航出版社,2000.
[81]陈廷良.现代运输机航空气象学[M].北京:气象出版社,1992.
[82] Yu J S. Airport Capacity and Regional Weather Modeling [D]. Lexington, MA:Massachusetts Institute of Technology,1994.
[83] Krozel Jimmy, William M. McNichols, et al. Causality Analysis for Aviation WeatherHazards[C]. The26th Congress of International Council of the Aeronautical Sciences(ICAS), Anchorage, Alaska,2008.
[84]赵树海.航空气象学[M].北京:气象出版社,1994.
[85] Keller J L. Data Requirements for Ceiling and Visibility Products Development[R].Lexington: Lincoln Laboratory, Massachusetts Institute of Technology,1994.
[86] Bergot T, Guedalia D. Numerical forecasting of radiation fog. Part I: numerical model andsensitivity tests[J]. Monthly Weather Review,1994:1218-1230.
[87] Tardif R. Characterizing Fog Occurrences In the Northeasteran United States UsingHistorical Data[C].11th Conference on Aviation, Range and Aerospace Meteorology,Hyannis MA,2004
[88] Hansen M, Winbin W. Empirical Analysis of Airport Capacity Enhancement Impacts: A CaseStudy of DFW Airport[R]. NEXTOR Research Report UCB-ITS-RR-1998-17,1998.
[89] FRON X. Performance indicators, targets, steering[C]. Technical Interchange meeting,Toulouse,2002.
[90] U.S. Department of Transportation, FAA. ORDER JO7210.55D[Z].2007.
[91] Hansen M, Post-deployment analysis of capacity and delay impacts of an airportenhancement: case of a new runway at Detroit[J]. Air Traffic Control Quarterly,2004,12(4):339-365.
[92] International Civil Aeronautics Organization. Air Traffic Management[Z].2007.
[93] Wells A T, Young S B. Airport planning and management[M]. New York: McGraw-Hill,2004.
[94] Ashford N, Wright P H. Airport engineering[M]. New York: A Wiley-IntersciencePublication.1984.
[95]余江.机场扩展终端区的运行优化策略研究[D].成都:西南交通大学,2005.
[96] Raguraman K. Getting Planes Off the Ground: Key Concepts and Issues in Airport CapacityPlanning and Management[R]. Institute of Transport Studies, The Australian Key Center inTransport Management, The University of Sydney and Monash University,1999.
[97] Schaefer L, Millner D. Flight delay propagation analysis with the detailed policy assessmenttool[C]. Proceedings of the Annual IEEE Systems, Man, and Cybernetics Conference,Tucson,2001.
[98] Subramanian P. A Simulation Study To Investigate Runway Capacity Using TAAM[D].Daytona Beach, Florida: Embry-Riddle Aeronautical University,2002.
[99] Sorensen J, Hunter G, Shen M. Requirements for Integrated Flight and Traffic ManagementDuring Final Approach[C]. IEEE Position Location and Navigation Symposium, Monterey,CA,1992.
[100] Glenn H P, Timothy J Dasey. A Statistical Analysis of Approach Winds atCapacity-Restricted Airports [C]. Proceedings of the Digital Avionics Systems Conference(DASC00), Philadelphia, PA,2004.
[101] Global Integrated Surface Hourly data [EB/OL].http://cdo.ncdc.noaa.gov/cgi-bin/cdo/cdostnsearch.pl
[102] Global Surface Summary of Day Data[EB/OL].http://www.ncdc.noaa.gov/cgi-bin/res40.pl?page=gsod.html
[103] Clark D A, Wilson F W. The Marine Stratus Initiative at San Francisco InternationalAirport[R]. Lexington, MA: Massachusetts Institute of Technology,1996.
[104] Evans J E, Dasey T J, Rhoda DA. Weather Sensing and Data Fusion to Improve Safety andReduce Delays at Major West Coast Airports[R]. Lexington: Lincoln Laboratory,Massachusetts Institute of Technology,1999.
[105] Marzban C, Leyton S, Colman B. Ceiling and Visibility Forecasts via Neural Networks[J].WEATHER AND FORECASTING,2007,22:466-479.
[106] FAA. Airport Benchmark Capacity Report[R]. Washington, D.C: U.S. Department ofTransportation,2004.
[107] Hilliker J L, Fritsch J M. An observations-based statistical system for warm-season hourlyprobabilistic forecasts of low ceiling at the San Francisco International Airport[J]. Appl.Meteor.,1999,38:1692–1705.
[108] Leipper D F. Fog on the U.S. West Coast: A review[J]. Bulletin of the AmericanMeteorological Society,1994,75:229–240.
[109] The Global ATM operational concept[C].11th Air Navigation Conference, Montreal,2003.
[110] Dlugi O, Asheimer T, Baldoni C, et al. The ATM Target Concept D3[R]. SESAR consortium,2007.
[111] Andrew F L, Sean M, Mahoney J. A Lead-time Metric For Assessing Skill in ForecastingThe Onset of IFR Events[C].13th Conference on Aviation, Range, and AerospaceMeteorology, New Orleans, LA,2008.
[112]盛骤,谢式千,潘承毅.概率论与数理统计[M].北京:高等教育出版社,2007.
[113]杜勇宏.季节时间序列理论与应用[M].天津:南开大学出版社,2008.
[114]董晓莉.时间序列数据挖掘相似性度量和周期模式挖掘研究[D].天津:天津大学,2007.
[115]冯红伟.数据挖掘技术的研究及应用[D].西安:西北工业大学,2002.
[116]董云影.基于遗传算法的模糊聚类技术的研究[D].大连:大连海事大学,2005.
[117]袁方,周志勇,宋鑫.初始聚类中心优化的k-means算法[J].计算机工程,2007,33(3):65-66.
[118] DUDA R O.模式分类[M].李宏东,姚天翔译.北京:机械工业出版社,2003.
[119] Clark DA. Characterizing the causes of low ceiling and visibility at U.S. airports[C].//6thConference on Aviation Weather Systems. Dallas, TX,1995:325-330.
[120] Tom M M. Machine Learning[M]. New York: McGraw-Hill,1997.
[121]杨行峻,郑君里.人工神经网络[M].北京:高等教育出版社,1992.
[122]刘棋.决策树ID3算法的改进研究[D].哈尔滨:哈尔滨工程大学,2009.
[123] Haykin S.神经网络原理[M].叶世伟,史忠植译.北京:机械工业出版社,2004.
[124]何述东等.多层前向神经网络结构的研究进展[J].控制理论与应用,1998,15(3):59-66.
[125] Ramamoorthy K, Hunter G. Modeling and Performance of NAS in Inclement Weather[C].6th AIAA Aviation, Integration, and Operations Conference. Wichita, KS,2006:25-27.
[126]张军,葛军,田俊杰等.概率天气预报及其应用[M].北京:气象出版社,1998.
[127]陆传赉.排队论[M].北京:北京邮电大学出版社,2009.