Air Route Crossing Angles Optimization Model with Different Preferences
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摘要: 为揭示交叉航路结构对空中交通影响的内部机理,有效打通航路节点运行瓶颈,研究了航路交叉角度优化问题. 首先,在分析航班飞行时间和航班油耗与航路基本相交结构角度关系的基础上,构建了交叉航路角度结构;其次,基于交叉航路交通量分布特征,建立了飞行时间和油耗偏好的交叉航路角度优化模型;最后,选取典型交叉航路进行了模型验证. 研究结果表明:航班飞行时间和油耗存在线性负相关关系;单一考虑时间优化时,航班总运行成本增加0.54%,仅考虑油耗优化时,总成本减少2.89%;无偏好优化时,航班总运行成本减少3.82%;考虑偏好时,在时间权重系数等于0.4处取得极小值,此时总运行成本降低5.26%,且空域内油耗密度、飞行冲突次数和管制员工作负荷3个指标的均值分别降低20.41%、56.12%、46.24%;优化后构型对原始空域结构的平均角度扰动为11.88%.Abstract: In order to investigate the internal mechanism of influence on air traffic posed by airspace structure and to clear bottlenecks of air traffic operation at air transit network nodes, the optimization of air route crossing angles was studied. After analysing the relationship between flight time, fuel consumption, and the angles of basic intersection route structure, an integrated crossing route angle structure was set up. Then, based on the air traffic flow distribution trait of the crossing routes, a preference mathematical model was developed to optimize the crossing angles in terms of flight time and fuel consumption. Finally, a representative crossing route was selected to verify the proposed model. The results show a negative linear correlation between flight time and fuel consumption and a 0.54% increase in total flight costs if flight time is solely considered versus a 2.89% decrease if only fuel consumption is taken into account. There is, however, a 3.82% reduction in total flight costs without preferences or 5.26% (the minimum value of total flight costs obtained when flight time coefficient equals 0.4) with preferences. The average amounts of fuel consumption density, flight conflicts, and controller workloads also declined 20.41%, 56.12%, and 46.24%, respectively, but the original airspace angle structure changed a mere 11.88% after the optimization.
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Key words:
- air traffic control /
- crossing air route /
- structure optimization /
- preference model /
- genetic algorithm
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表 1 各结构交通量均值和标准差
Table 1. Mean and standard deviation of flight numbers
计算项目 ${F_{{\rm R}_{(1,2)}}}$ ${F_{{\rm R}_{(3,4)}}}$ ${F_{{\rm R}_{(1,4)}}}$ ${F_{{\rm R}_{(2,3)}}}$ 均值/架次 636 682 7 40 标准差 7.456 0 5.148 4 1.324 2 2.225 3 表 2 不同偏好对应的优化结果
Table 2. Optimization results for different preferences
优化指标 原始结构 仅优化时间 仅优化油耗 总飞行时间/h 541.6 468.0 592.8 总油耗/t 752.3 811.1 682.8 总成本/千元 5 695.4 5 726.2 5 530.6 时间变化率/% 0 –13.60 9.45 油耗变化率/% 0 7.81 –9.24 总成本变化率/% 0 0.54 –2.89 表 3 综合优化各结构对应角度
Table 3. Angles of each structure for comprehensive optimization
项目 汇聚 分散 转弯1 转弯2 优化前 54 51 33 72 优化后 61 57 50 68 表 4 综合优化结果
Table 4. Results of comprehensive optimization
项目 总时间/h 总油耗/t 总成本/千元 优化前 541.6 752.3 5 695.4 优化后 513.9 728.6 5 477.9 优化百分比/% –5.11 –3.15 –3.82 表 5 优化前后各评价指标结果
Table 5. Optimization results before and after optimization
项目 总时间/h 总油耗/t 总成本/千元 优化前 436.7 715.4 5 136.2 优化后 418.3 696.9 4 978.1 优化百分比/% –4.22 –2.58 –3.08 -
李善梅,徐肖豪,王超,等. 基于灰色聚类的交叉航路拥挤识别方法[J]. 西南交通大学学报,2015,50(1): 189-197 doi: 10.3969/j.issn.0258-2724.2015.01.028LI Shanmei, XU Xiaohao, WANG Chao, et al. Congestion identification of crossing air routes based on gray clustering method[J]. Journal of Southwest Jiaotong University, 2015, 50(1): 189-197 doi: 10.3969/j.issn.0258-2724.2015.01.028 CAI Kaiquan, ZHANG Jun, DU Wenbo, et al. Analysis of the Chinese air route network as a complex network[J]. Chinese Physics B, 2012, 21(2): 596-602 WANG Shijin, CAO Xi, LI Haiyun, et al. Air route network optimization in fragmented airspace based on cellular automata[J]. Chinese Journal of Aeronautics, 2017, 30(3): 1184-1195 doi: 10.1016/j.cja.2017.04.002 ZHOU Chi, ZHANG Xuejun, CAI Kaiquan, et al. Comprehensive learning multi-objective particle swarm optimizer for crossing waypoints location in air route network[J]. Chinese Journal of Electronics, 2011, 20(3): 534-538 JIN C, ZHU Y B, FANG J, et al. An improved methodology for ARN crossing waypoints location problem[C]//Proceedings of the 31st digital avionics systems conference (DASC). Williamsburg: IEEE, 2012: 4A5-1-4A5-9 陈才龙. 基于复杂网络的航路汇聚点布局优化方法研究[D]. 合肥: 中国科学技术大学, 2011 WANG S J, GONG Y H. Research on air route network nodes optimization with avoiding the three areas[J]. Safety Science, 2014, 66(7): 9-18 doi: 10.1016/j.ssci.2014.01.008 SEONGIM C, DANIEL G M, JOHN E R, et al. Design of an optimal route structure using heuristics-based stochastic schedulers[J]. Journal of Aircraft, 2015, 52(3): 764-777 doi: 10.2514/1.C032645 张进,胡明华,张晨. 交叉航路空域的时隙可用性评估方法[J]. 西南交通大学学报,2010,45(6): 958-964 doi: 10.3969/j.issn.0258-2724.2010.06.023ZHANG Jin, HU Minghua, ZHANG Chen. Evaluation method for time-slot availability of crossing air route[J]. Journal of Southwest Jiaotong University, 2010, 45(6): 958-964 doi: 10.3969/j.issn.0258-2724.2010.06.023 戴福青,郑哲. 基于航班流的航路交叉点结构研究[J]. 计算机仿真,2016,33(8): 22-25,452 doi: 10.3969/j.issn.1006-9348.2016.08.005DAI Fuqing, ZHENG Zhe. The architecture study of enroute intersection based on flight flows[J]. Computer Simulation, 2016, 33(8): 22-25,452 doi: 10.3969/j.issn.1006-9348.2016.08.005 韩松臣,曲玉玲,孙樊荣,等. 航路交叉点处碰撞风险模型[J]. 西南交通大学学报,2013,48(2): 383-389 doi: 10.3969/j.issn.0258-2724.2013.02.029HAN Songchen, QU Yuling, SUN Fanrong, et al. Collision risk model around intersection of airways[J]. Journal of Southwest Jiaotong University, 2013, 48(2): 383-389 doi: 10.3969/j.issn.0258-2724.2013.02.029 王莉莉,张潇潇. 航路交叉点容量及航路容量模型研究[J]. 中国民航大学学报,2015,33(5): 7-10 doi: 10.3969/j.issn.1674-5590.2015.05.002WANG Lili, ZHANG Xiaoxiao. Research on route crossing point capacity and route capacity models[J]. Journal of Civil Aviation University of China, 2015, 33(5): 7-10 doi: 10.3969/j.issn.1674-5590.2015.05.002 叶博嘉,胡明华,张晨,等. 基于交通结构的空中交通复杂性建模[J]. 交通运输系统工程与信息,2012,12(1): 166-172 doi: 10.3969/j.issn.1009-6744.2012.01.025YE Bojia, HU Minghua, ZHANG Chen, et al. Traffic structure-based air traffic complexity modeling[J]. Journal of Transportation Systems Engineering and Information Technology, 2012, 12(1): 166-172 doi: 10.3969/j.issn.1009-6744.2012.01.025 王超,郑旭芳,王蕾. 交汇航路空中交通流的非线性特征研究[J]. 西南交通大学学报,2017,52(1): 171-178 doi: 10.3969/j.issn.0258-2724.2017.01.024WANG Chao, ZHENG Xufang, WANG Lei. Research on nonlinear characteristics of air traffic flows on converging air routes[J]. Journal of Southwest Jiaotong University, 2017, 52(1): 171-178 doi: 10.3969/j.issn.0258-2724.2017.01.024 中国民用航空局发展计划司. 从统计看民航2016版[M]. 北京: 中国民航出版社, 2016: 7-13 冯霞,张鑫,陈锋. 飞机过站上客过程持续时间分布[J]. 交通运输工程学报,2017,17(2): 98-105 doi: 10.3969/j.issn.1671-1637.2017.02.011FENG Xia, ZHANG Xin, CHEN Feng. Boarding duration distribution of aircraft turnaround[J]. Journal of Traffic and Transportation Engineering, 2017, 17(2): 98-105 doi: 10.3969/j.issn.1671-1637.2017.02.011 王如华,叶叶沛,任启鸿. 商用飞机成本指数方法及其案例研究[J]. 民用飞机设计与研究,2015(4): 23-25,78 doi: 10.3969/j.issn.1674-9804.2015.04.008WANG Ruhua, YE Yepei, REN Qihong. Case study of cost index for commercial aircraft[J]. Technology Research, 2015(4): 23-25,78 doi: 10.3969/j.issn.1674-9804.2015.04.008 DEB K, PRATAP A, AGAWAL S, et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197 doi: 10.1109/4235.996017