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A dynamic adjustment and distribution method of air traffic flow en-route

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  • Zhang, Zhaoning
  • Hao, Zhongzhou
  • Gao, Zheng

Abstract

To improve the restrictions of flow distribution caused by the static air traffic flow distribution mode and alleviate the conflicts between the current flight traffic demand and the resource constraints of airspace, the authors propose a flight flow dynamic adjustment and distribution model based on predatory thought and construct an en-route traffic load balance model. Flight flow dynamic adjustment distribution model is based on comparing the current traffic en-route, the capacity en-route, the change rate of traffic en-route and other factors. The ground holding policy and the route selection policy are combined to optimize the predator thought. An equilibrium of traffic load is achieved with a dynamic adjustment and distribution of each route in accordance with the current traffic condition of each route by cycle. Model the en-route traffic load balance model based on the geometric relationship of the traffic load. Finally, we use matlab 2010b for numerical example and the results show the effectiveness of the model and algorithm can make the flight traffic of routes in balance.

Suggested Citation

  • Zhang, Zhaoning & Hao, Zhongzhou & Gao, Zheng, 2015. "A dynamic adjustment and distribution method of air traffic flow en-route," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 15-20.
    • Handle: RePEc:eee:jaitra:v:42:y:2015:i:c:p:15-20
    DOI: 10.1016/j.jairtraman.2014.07.007
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    References listed on IDEAS

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    1. Guglielmo Lulli & Amedeo Odoni, 2007. "The European Air Traffic Flow Management Problem," Transportation Science, INFORMS, vol. 41(4), pages 431-443, November.
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    Cited by:

    1. Wang, Yu-Chen & Wong, Jinn-Tsai, 2019. "Exploring air network formation and development with a two-part model," Journal of Transport Geography, Elsevier, vol. 75(C), pages 122-131.
    2. Cai, Kaiquan & Shen, Zhiqi & Luo, Xiaoyan & Li, Yue, 2023. "Temporal attention aware dual-graph convolution network for air traffic flow prediction," Journal of Air Transport Management, Elsevier, vol. 106(C).

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