期刊文献+

航路扇区容量需求的交通流动力学推演与预测 被引量:2

Capacity Demand Prediction for En-route Airspace Based on Network Traffic Flow Dynamic Model
下载PDF
导出
摘要 为科学应对交通需求的持续增长,合理进行空域规划,从介观层面刻画交通流动态演化过程,提出航路网络交通流演化模型.以此为基础,将预测得到的城市对间年度交通总需求量推演到整个航路网络中,实现未来交通需求时空分布的中长期预测;构建航路扇区容量需求预测模型,选取华东地区两个相邻航路扇区为研究对象,进行实例验证.结果表明,所提方法能对航路扇区容量需求进行有效预测,未来5年内两个航路扇区容量需求将分别由50架次/h、39架次/h上升为70架次/h、45架次/h. This paper proposes a prediction method for the future capacity demand of en-route sectors,which can be further used to airspace planning as traffic demand continues growing in the future.An en-route network traffic flow dynamic model,characterizing dynamics of traffic flow,is proposed to describe dynamic evolution process of traffic flow in the air route network.Then the yearly city-pair air traffic demand can be propagated into the entire en-route network,and the temporal and spatial traffic demand in the en-route airspace is obtained.On this basis,the future capacity demand of en-route airspace is predicted based on the time and space distribution of traffic demand in the network.A case study of two en-route sectors demonstrates the effectiveness of the proposed prediction framework The result indicates that the capacity demands of the two sectors will increase from 50 aircraft/h and 39 aircraft/h to 70 aircraft/h and 45 aircraft/h in the next 5 years.
作者 陈丹 尹嘉男 CHEN Dan;YIN Jia-nan(School of Automotive&Rail Transit,Nanjing Institute of Technology,Nanjing 211167,China;College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处 《交通运输系统工程与信息》 EI CSCD 北大核心 2020年第3期206-211,共6页 Journal of Transportation Systems Engineering and Information Technology
基金 国家自然科学基金(61903185) 江苏省自然科学基金(BK20191014) 南京工程学院高层次引进人才科研启动基金(YKJ201842).
关键词 航空运输 容量需求预测 交通流动力学演化模型 航路扇区容量 空域规划 air transportation capacity demand prediction evolution model of network traffic flow capacity of en-route sector airspace planning
  • 相关文献

参考文献1

二级参考文献15

  • 1赵玉环,石新华.基于时间序列的空中交通流量灰预测模型算法[J].中国民航大学学报,2007,25(6):54-57. 被引量:6
  • 2中国民用航空局发展计划司.从统计看民航2013[M].北京:中国民航出版社,2013:126-147.
  • 3ONDER E, KUZU S. Forecasting air traffic volumes using smoothing techniques[ J ]. Journal of Aeronautics and Space Technologies, 2014, 7 (1) : 65-70.
  • 4BOUGAS C. Forecasting air canada: an evaluation of combination methods[ D ]. Bougas, 2013. passenger traffic flows in time series models and [ S. 1. ] : Constantinos.Bougas, 2013.
  • 5MALLAT S G. A theory for muhiresolution signal decomposition: the wavelet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11 (7) : 674-693.
  • 6MENON P K, SWERIDUK G D, BILIMORIA K D. New approach for modeling, analysis, and control of air traffic flow[J]. Journal of Guidance, Control, and Dynamics, 2004, 27(5): 737-744.
  • 7ROY S, SRIDHAR B, ~ERGHESE G C. An aggregate dynamic stochastic model for an air traffic system[ C]// Proceedings of the 5th Eurocontrol/Federal Aviation Agency Air Traffic Management Research and Development Seminar. Budapest: [ s. n. ], 2003: 1-10.
  • 8SRIDHAR B, SONI T, SHETH K, et al. Aggregate flow model for air-traffic management[ J 1- Journal of Guidance, Control, and Dynamics, 2006, 29 (4): 992 -997.
  • 9SRIDHAR B, CHEN N Y, NG H K. An aggregate sector flow model for air traffic demand forecasting[C]/J 9th AIAA Aviation Technology, Integration, and Operations Conference (ATIO). [ S. 1. ] : NASA Ames Research Center, 2009- 1-12.
  • 10PETRIS G, PETRONE S, CAMPAGNOLI P. Dynamic linear modds with RIM]. New York: Springer- Verlag, 2009: 41-74.

共引文献10

同被引文献23

引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部