离港飞机滑行路径优化与仿真
|
摘要
可变滑行时间是评估机场场面交通流特性的重要指标,其影响机场的运行效率、旅客满意度及污染物排放。以国内某大型枢纽机场为例,构建飞机离场场面交通流特性的模型。通过分析离港交通流的特性规则、飞机在离港滑行路径中的拥塞规律,根据元胞自动机原理,将机场场面滑行区域的交通流视作由节点和链路构成的机场网络拓扑结构,以飞机滑行时的规则和飞机之间可能发生的冲突作为约束控制条件。使用蒙特卡洛算法得出最优滑行路径,计算出最小滑行时间。最后,结合航班运行数据对所建模型进行仿真分析。结果表明,该模型能有效减少离港飞机的滑行等待时间,缓解机场场面交通流的拥堵情况,充分调度和合理分配机场地面资源。
Variable taxiing time is an important index to evaluate the characteristics of airport traffic flow, which affects airport operation efficiency, passenger satisfaction and pollutant emission. Taking a large domestic hub airport as instance, the aircraft departure traffic flow model is constructed. Through analyses of departure traffic flow characteristics and congestion rules during departure taxiing path, and according to cellular automata theory,traffic flow of airport taxiing area is regarded as airport network topology structure made of node and link, taking taxiing rule and possible conflicts between aircrafts as constrained control conditions. Monte-Carlo algorithm is employed to achieve optimal taxiing path and shortest taxiing time. Finally, model simulation is conducted based on operational flight data. Result shows that the current model can effectively reduce taxiing time of departure flights, alleviate traffic congestion, fully schedule and reasonably allocate airport ground resource.
Variable taxiing time is an important index to evaluate the characteristics of airport traffic flow, which affects airport operation efficiency, passenger satisfaction and pollutant emission. Taking a large domestic hub airport as instance, the aircraft departure traffic flow model is constructed. Through analyses of departure traffic flow characteristics and congestion rules during departure taxiing path, and according to cellular automata theory,traffic flow of airport taxiing area is regarded as airport network topology structure made of node and link, taking taxiing rule and possible conflicts between aircrafts as constrained control conditions. Monte-Carlo algorithm is employed to achieve optimal taxiing path and shortest taxiing time. Finally, model simulation is conducted based on operational flight data. Result shows that the current model can effectively reduce taxiing time of departure flights, alleviate traffic congestion, fully schedule and reasonably allocate airport ground resource.
引文
[1]李楠,吕弘哲.基于场面监视数据航空器场面滑行路径确定[J].航空计算技术,2016,46(2):6-9.
[2]谷润平,崔朋,唐建勋,等.基于D*算法的场面滑行动态规划研究[J].科学技术与工程,2015,15(1):315-319.
[3]LEE H,BALAKRISHNAN H.Optimization of Airport Taxiway Operations at Detroit Metropolitan Airport(DTW)[C]//10th AIAA Aviation Technology,Integration and Operations(ATIO)Conference,Fort Worth,Texas,Sept 13-15,2010-9151:1-16.
[4]LEGGE J,LEVY B.Departure Taxi Time Predictions Using ASDE-X Surveillance Data[C]//26th Congress of ICAS and 8th AIAA ATIO,Anchorage,Alaska,Sept 14-19,2008-8958:1-7.
[5]钟石泉,马寿峰,贺国光,等.交通诱导系统路网状态描述方法及仿真研究[J].系统工程学报,2009,24(1):25-31.
[6]仉俊峰,王昉,苏永涛.用于可靠性评测的优化检测方法[J].东北林业大学学报,2009,37(8):112-114.
[7]CLARKE J P,REN L,MCCLAIN E,et al.Evaluating Concepts for Metroplex Operations[M].Chicago:University of Chicago Press,2013.
[8]LORDAN O,SALLAN J M,SIMO P,et al.Robustness of the air transport network[J].Transportation Research Part E:Logistics and Transportation Review,2014,68:155-163.
[9]TANDALE M D,SENGUPTA P,MENON P K,et al.Queuing network models of the national airspace system[J].AIAA Journal,2008,9(1):6-20.
[10]BOWEN E G,PEARCEY T.Delays in the flow of air traffic[J].J Royal Aero Soc,1948,52(448):251-258.
[11]张洪海,杨磊,别翌荟,等.终端区进场交通流广义跟驰行为与复杂相变分析[J].航空学报,2015,36(3):949-961.
[12]戴文雯,别翌荟,张洪海,等.基于Net Logo的终端区交通流仿真[J].航空计算技术,2014,44(1):30-33.
[13]JANIC'M,TO譒IC'V.Terminal airspace capacity model[J].Transportation Research Part A General,1982,16(4):253-260.
[14]SONG LIXIA,WANKE C R,ZOBELL S,et al.Methodologies of Estimating the Impact of Severe Weather on Airspace Capacity[C]//26th Congress of ICAS and 8th AIAA ATIO,Anchorage,Alaska,Sept 14-19,2008-8917:1-12.
[15]PYRGIOTIS N,MALONE K M,ODONI A.Modelling delay propagation within an airport network[J].Transportation Research Part C:Emerging Technologies,2013,20(2):60-75.
[2]谷润平,崔朋,唐建勋,等.基于D*算法的场面滑行动态规划研究[J].科学技术与工程,2015,15(1):315-319.
[3]LEE H,BALAKRISHNAN H.Optimization of Airport Taxiway Operations at Detroit Metropolitan Airport(DTW)[C]//10th AIAA Aviation Technology,Integration and Operations(ATIO)Conference,Fort Worth,Texas,Sept 13-15,2010-9151:1-16.
[4]LEGGE J,LEVY B.Departure Taxi Time Predictions Using ASDE-X Surveillance Data[C]//26th Congress of ICAS and 8th AIAA ATIO,Anchorage,Alaska,Sept 14-19,2008-8958:1-7.
[5]钟石泉,马寿峰,贺国光,等.交通诱导系统路网状态描述方法及仿真研究[J].系统工程学报,2009,24(1):25-31.
[6]仉俊峰,王昉,苏永涛.用于可靠性评测的优化检测方法[J].东北林业大学学报,2009,37(8):112-114.
[7]CLARKE J P,REN L,MCCLAIN E,et al.Evaluating Concepts for Metroplex Operations[M].Chicago:University of Chicago Press,2013.
[8]LORDAN O,SALLAN J M,SIMO P,et al.Robustness of the air transport network[J].Transportation Research Part E:Logistics and Transportation Review,2014,68:155-163.
[9]TANDALE M D,SENGUPTA P,MENON P K,et al.Queuing network models of the national airspace system[J].AIAA Journal,2008,9(1):6-20.
[10]BOWEN E G,PEARCEY T.Delays in the flow of air traffic[J].J Royal Aero Soc,1948,52(448):251-258.
[11]张洪海,杨磊,别翌荟,等.终端区进场交通流广义跟驰行为与复杂相变分析[J].航空学报,2015,36(3):949-961.
[12]戴文雯,别翌荟,张洪海,等.基于Net Logo的终端区交通流仿真[J].航空计算技术,2014,44(1):30-33.
[13]JANIC'M,TO譒IC'V.Terminal airspace capacity model[J].Transportation Research Part A General,1982,16(4):253-260.
[14]SONG LIXIA,WANKE C R,ZOBELL S,et al.Methodologies of Estimating the Impact of Severe Weather on Airspace Capacity[C]//26th Congress of ICAS and 8th AIAA ATIO,Anchorage,Alaska,Sept 14-19,2008-8917:1-12.
[15]PYRGIOTIS N,MALONE K M,ODONI A.Modelling delay propagation within an airport network[J].Transportation Research Part C:Emerging Technologies,2013,20(2):60-75.