IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v152y2021ics1366554521001745.html
   My bibliography  Save this article

A fuel savings and benefit analysis of reducing separation standards in the oceanic airspace managed by the New York Air Route Traffic Control Center

Author

Listed:
  • Li, Tao
  • Wan, Yan

Abstract

New or improved satellite-based technologies are being introduced to improve the surveillance and communication capabilities in remote airspace. We study the benefits of reducing the separation standards among flights in the oceanic airspace managed by the New York Air Route Traffic Control Center (New York Oceanic) based on these technologies. We develop a model that simulates the activities of aircraft, pilots, and air traffic controllers (ATC) at the microscopic level to study the benefits of doing so in 2020 and 2025. With pessimistic assumptions on the reduced separation standards, the system-wide fuel savings within New York Oceanic are about (in million gallons) 2.25 in 2020 and 3.21 in 2025. After excluding additional variable cost, the monetary value of the fuel savings is about (in million 2018 US dollars) 3.65 and 6.38, respectively. The fuel benefits are more significant for aircraft with light or medium maximum takeoff weight. Some determinants of the workload of ATC and pilots can reduce by about 10% to 20%. With optimistic assumptions on the reduced standards, the corresponding statistics are about 2 to 3 times as high. This study can be used, for example, by air traffic control agencies to conduct benefit-cost analyses of adopting new/improved technologies, by airlines to develop strategies to make the best use of satellite services, and by satellite service providers to design service charging schemes and conduct market analysis.

Suggested Citation

  • Li, Tao & Wan, Yan, 2021. "A fuel savings and benefit analysis of reducing separation standards in the oceanic airspace managed by the New York Air Route Traffic Control Center," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:transe:v:152:y:2021:i:c:s1366554521001745
    DOI: 10.1016/j.tre.2021.102407
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554521001745
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2021.102407?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tao Li, 2021. "An Optimization Model for Selecting Sample Days," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 38(04), pages 1-22, August.
    2. A. Alonso-Ayuso & L. Escudero & F. Martín-Campo, 2014. "On modeling the air traffic control coordination in the collision avoidance problem by mixed integer linear optimization," Annals of Operations Research, Springer, vol. 222(1), pages 89-105, November.
    3. Yi Yang & Ying Nan & Ming Tong, 2018. "Cooperative Route Planning for Multiple Aircraft in a Semifree ATC System," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, March.
    4. Sibdari, Soheil & Mohammadian, Iman & Pyke, David F., 2018. "On the impact of jet fuel cost on airlines’ capacity choice: Evidence from the U.S. domestic markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 1-17.
    5. Khan, Waqar Ahmed & Chung, Sai-Ho & Ma, Hoi-Lam & Liu, Shi Qiang & Chan, Ching Yuen, 2019. "A novel self-organizing constructive neural network for estimating aircraft trip fuel consumption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 72-96.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Xinting & Hong, Ning & He, Fang & Lin, Yu & Li, Lishuai & Fu, Xiaowen, 2023. "Predicting aircraft trajectory uncertainties for terminal airspace design evaluation," Journal of Air Transport Management, Elsevier, vol. 113(C).
    2. Chu, Nana & Ng, Kam K.H. & Liu, Ye & Hon, Kai Kwong & Chan, Pak Wai & Li, Jianbing & Zhang, Xiaoge, 2024. "Assessment of approach separation with probabilistic aircraft wake vortex recognition via deep learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Linlin Chen & Shuihua Han & Chaokan Du & Zongwei Luo, 2022. "A real-time integrated optimization of the aircraft holding time and rerouting under risk area," Annals of Operations Research, Springer, vol. 310(1), pages 7-26, March.
    2. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    3. Wen, Xin & Sun, Xuting & Sun, Yige & Yue, Xiaohang, 2021. "Airline crew scheduling: Models and algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    4. Sun, Yige & Chung, Sai-Ho & Wen, Xin & Ma, Hoi-Lam, 2021. "Novel robotic job-shop scheduling models with deadlock and robot movement considerations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    5. Wen, Xin & Ma, Hoi-Lam & Chung, Sai-Ho & Khan, Waqar Ahmed, 2020. "Robust airline crew scheduling with flight flying time variability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    6. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    7. Khan, Waqar Ahmed & Ma, Hoi-Lam & Ouyang, Xu & Mo, Daniel Y., 2021. "Prediction of aircraft trajectory and the associated fuel consumption using covariance bidirectional extreme learning machines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    8. Alonso-Ayuso, Antonio & Escudero, Laureano F. & Martín-Campo, F. Javier, 2016. "Multiobjective optimization for aircraft conflict resolution. A metaheuristic approach," European Journal of Operational Research, Elsevier, vol. 248(2), pages 691-702.
    9. Hofmann, Erik & Solakivi, Tomi & Töyli, Juuso & Zinn, Martin, 2018. "Oil price shocks and the financial performance patterns of logistics service providers," Energy Economics, Elsevier, vol. 72(C), pages 290-306.
    10. Salman Arif & Jason Atkin & Geert Maere, 2023. "Analysing the benefits of trajectory deviations for planar trajectory optimisation," Annals of Operations Research, Springer, vol. 326(1), pages 537-560, July.
    11. Khan, Waqar Ahmed & Chung, Sai-Ho & Ma, Hoi-Lam & Liu, Shi Qiang & Chan, Ching Yuen, 2019. "A novel self-organizing constructive neural network for estimating aircraft trip fuel consumption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 72-96.
    12. Yong Tian & Bojia Ye & Marc Sáez Estupiñá & Lili Wan, 2018. "Stochastic Simulation Optimization for Route Selection Strategy Based on Flight Delay Cost," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(06), pages 1-24, December.
    13. Wenjie Li & Jialing Dai & Yi Xiao & Shengfa Yang & Chenpeng Song, 2021. "Estimating waterway freight demand at Three Gorges ship lock on Yangtze River by backpropagation neural network modeling," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(3), pages 495-521, September.
    14. Abdelghany, Ahmed & Abdelghany, Khaled & Azadian, Farshid, 2023. "The airline seat capacity allocation problem: An expected marginal profit approach," Journal of Air Transport Management, Elsevier, vol. 112(C).
    15. Chow, Clement Kong Wing & Tsui, Wai Hong Kan & Wu, Hanjun, 2021. "Airport subsidies and domestic inbound tourism in China," Annals of Tourism Research, Elsevier, vol. 90(C).
    16. Anshu Agrawal, 2021. "Sustainability of airlines in India with Covid-19: Challenges ahead and possible way-outs," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(4), pages 457-472, August.
    17. Pal, Debdatta & Mitra, Subrata K., 2022. "Do airfares respond asymmetrically to fuel price changes? A multiple threshold nonlinear ARDL model," Energy Economics, Elsevier, vol. 111(C).
    18. Antonio Alonso-Ayuso & Laureano F. Escudero & F. Javier Martín-Campo, 2016. "Exact and Approximate Solving of the Aircraft Collision Resolution Problem via Turn Changes," Transportation Science, INFORMS, vol. 50(1), pages 263-274, February.
    19. Chung, Sai-Ho, 2021. "Applications of smart technologies in logistics and transport: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    20. Sadeque Hamdan & Oualid Jouini & Ali Cheaitou & Zied Jemai & Tobias Andersson Granberg, 2023. "On the binary formulation of air traffic flow management problems," Annals of Operations Research, Springer, vol. 321(1), pages 267-279, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transe:v:152:y:2021:i:c:s1366554521001745. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.