基于时间的进场航班效率评估方法研究
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摘要
终端区作为空中交通的起讫点,是拥堵、延误的多发区,其运行效率对航班整体的运行效率至关重要。欧洲航行安全组织(EUROCONTROL)采用畅通进场时间(Unimpeded AMSA Time)作为量化进场效率的标杆。我国目前还没有明确的进场效率量化指标,因此可以借鉴畅通进场时间这个指标来评估进场效率。以首都机场为研究对象,首先对进港航班ADS-B数据进行预处理来计算进场时间,然后利用K-means聚类算法对航班进行分组,运用回归分析法找寻与终端区拥挤程度关联性最强的解释变量,提出关于畅通航班拥挤阈值的方法来计算畅通进场时间,最后用时间效率指标对进场效率进行评估,得出5月首都机场的进场效率为83. 39%,可为进场效能评估提供参考。
This paper is aimed at a discussion of the potential cause and reason of the arrival efficiency in the air traffic management by taking the reference to the European arrival sequence and metering area. In so doing,we have made a discussion of the potential causes of the arrival sequencing and metering areaon the arrival efficiency in this paper according to the actual situation of Beijing Airport by determining the surrounding scope of100 nautical miles of Beijing terminal and quantifying the arrival efficiency of the terminal by developing the flight efficiency metrics based on the essential quantitative parameter of the unimpeded arrival time. To achieve the above said purpose,first of all,it is necessary to calculate the ADS-B data of the arrival flights and extract the position coordinates of each flight at 100 nautical miles from the airport benchmark point respectively. And,then,it is necessary to work out the entry-point time,regulate the landing time and take into account the actual arrival time for each flight with the interpolation method. And, secondly, all the flights involved should be classified into 6 directions( the Northeast,Dalian,China East,China South,China Southwest,and China Northwest) by K-means clustering algorithm and next grouped in accordance with their entry and landing ways. And,thirdly,it is necessary to choose the metric of the number of approach queue with the strongest correlation with the arrival time through regression analysis. And,finally,it would be possible to work out the efficiency of the terminal of Capital Airport via the actual ADS-B data. The afforementioned analysis we have done on the relation between the mean arrival time and the number of the arrival queue shows that in the terminal,the arrival time remains basically unchanged if the number of arrival queue is small while the number of arrival queue increases linearly,for the part of the arrival time remains basically unchanged with no effect of the queue congestion. Such a situation can be made known as unimpeded flights. And,therefore,we suggest to calculate the unimpeded arrival time by means of the flight congestion threshold. Thus,in turn,the calculation results of the data show that the proposed method tends to be close to the unimpeded arrival time gained by the standard value method,hence,verifying the reliability of the method we have gained. The final assessment of the arrival efficiency of the Capital Airport in May,2016,turns out to be 83. 39%,which is valuable to provide incentive for further research related to the flight efficiency.
This paper is aimed at a discussion of the potential cause and reason of the arrival efficiency in the air traffic management by taking the reference to the European arrival sequence and metering area. In so doing,we have made a discussion of the potential causes of the arrival sequencing and metering areaon the arrival efficiency in this paper according to the actual situation of Beijing Airport by determining the surrounding scope of100 nautical miles of Beijing terminal and quantifying the arrival efficiency of the terminal by developing the flight efficiency metrics based on the essential quantitative parameter of the unimpeded arrival time. To achieve the above said purpose,first of all,it is necessary to calculate the ADS-B data of the arrival flights and extract the position coordinates of each flight at 100 nautical miles from the airport benchmark point respectively. And,then,it is necessary to work out the entry-point time,regulate the landing time and take into account the actual arrival time for each flight with the interpolation method. And, secondly, all the flights involved should be classified into 6 directions( the Northeast,Dalian,China East,China South,China Southwest,and China Northwest) by K-means clustering algorithm and next grouped in accordance with their entry and landing ways. And,thirdly,it is necessary to choose the metric of the number of approach queue with the strongest correlation with the arrival time through regression analysis. And,finally,it would be possible to work out the efficiency of the terminal of Capital Airport via the actual ADS-B data. The afforementioned analysis we have done on the relation between the mean arrival time and the number of the arrival queue shows that in the terminal,the arrival time remains basically unchanged if the number of arrival queue is small while the number of arrival queue increases linearly,for the part of the arrival time remains basically unchanged with no effect of the queue congestion. Such a situation can be made known as unimpeded flights. And,therefore,we suggest to calculate the unimpeded arrival time by means of the flight congestion threshold. Thus,in turn,the calculation results of the data show that the proposed method tends to be close to the unimpeded arrival time gained by the standard value method,hence,verifying the reliability of the method we have gained. The final assessment of the arrival efficiency of the Capital Airport in May,2016,turns out to be 83. 39%,which is valuable to provide incentive for further research related to the flight efficiency.
引文
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[4] GULDING J,KNORR D,ROSE M,et al. US/Europecomparison of atm-related operational performance[J].Air Traffic Control Quarterly,2016,18(1):5-27.
[5] European Organisation for the Safety of Air Navigation,Federal Aviation Administration. US/Europe comparisonof ATM-related operational performance 2015[R]. Brus-sels:Performance Review Unit,Performance Analysis Of-fice,2015.
[6] Civil Air Navigation Service Organization. Methodologiesfor calculating delays/improvement opportunity pools byphase of flight[R]. Montreal:Civil Air Navigation Serv-ices Organization,2013.
[7] WU Kangkang(吴抗抗). Study on flight delay and fore-casting and early warning methods for derived events(航班延误及其衍生事件预测预警方法研究)[D]. Nanjing:Nanjing University of Aeronautics,2012.
[8] WANG Min(王敏). Performance evaluation of energy-saving and emission-reduction of ATC based on flight effi-ciency[J]. Aviation Computing Technology(航空计算技术),2015,45(6):41-44.
[9] WANG Pengpeng(王鹏鹏). Terminal efficiency evalua-tion method and model study(终端区运行效率评估方法及模型研究)[D]. Nanjing:Nanjing University of Aero-nautics,2016.
[10] ZHOU Aiwu(周爱武),YU Yafei(于亚飞). Researchon K-means clustering algorithm[J]. Computer Tech-nology and Development(计算机技术与发展),2011,21(2):62-65.
[11] REYNOLDS T G. Air traffic management performanceassessment using flight inefficiency metrics[J]. Trans-port Policy,2014,34(2):63-74.
[2] European organisation for the safety of air navigation. airtraffic management airport performance(ATMAP)frame-work-measuring airport airside and nearby airspace per-formance[R]. Brussels:Performance Review Commis-sion,2009.
[3] European Organisation for the Safety of Air Navigation. Ad-ditional ASMA time performance indicator document[R].Brussels:PRU Quality Management System,2015.
[4] GULDING J,KNORR D,ROSE M,et al. US/Europecomparison of atm-related operational performance[J].Air Traffic Control Quarterly,2016,18(1):5-27.
[5] European Organisation for the Safety of Air Navigation,Federal Aviation Administration. US/Europe comparisonof ATM-related operational performance 2015[R]. Brus-sels:Performance Review Unit,Performance Analysis Of-fice,2015.
[6] Civil Air Navigation Service Organization. Methodologiesfor calculating delays/improvement opportunity pools byphase of flight[R]. Montreal:Civil Air Navigation Serv-ices Organization,2013.
[7] WU Kangkang(吴抗抗). Study on flight delay and fore-casting and early warning methods for derived events(航班延误及其衍生事件预测预警方法研究)[D]. Nanjing:Nanjing University of Aeronautics,2012.
[8] WANG Min(王敏). Performance evaluation of energy-saving and emission-reduction of ATC based on flight effi-ciency[J]. Aviation Computing Technology(航空计算技术),2015,45(6):41-44.
[9] WANG Pengpeng(王鹏鹏). Terminal efficiency evalua-tion method and model study(终端区运行效率评估方法及模型研究)[D]. Nanjing:Nanjing University of Aero-nautics,2016.
[10] ZHOU Aiwu(周爱武),YU Yafei(于亚飞). Researchon K-means clustering algorithm[J]. Computer Tech-nology and Development(计算机技术与发展),2011,21(2):62-65.
[11] REYNOLDS T G. Air traffic management performanceassessment using flight inefficiency metrics[J]. Trans-port Policy,2014,34(2):63-74.