Prediction of Off-Block Time Distribution for Departure Metering

被引:0
|
作者
Mori, Ryota [1 ]
机构
[1] Graduate School of Maritime Sciences, Electronic Navigation Research Institute, Kobe University, Kobe,658-0022, Japan
来源
Journal of Air Transportation | 2024年 / 32卷 / 03期
基金
日本学术振兴会;
关键词
D O I
10.2514/1.D0359
中图分类号
学科分类号
摘要
The uncertainties related to target off-block time (TOBT), the pushback-ready time predicted by aircraft operators, affect greatly airport operations. The accuracy of TOBT is, in general, difficult to be improved, because there are many uncertain factors in the departure process, e.g., delays in the passengers’ boarding. A better understanding of TOBT uncertainties, however, may help to improve airport surface operations. Currently, TOBT is estimated as a single point in time and updated as necessary by aircraft operators. Instead, the author proposes that TOBT is estimated as a distribution with a Johnson-SU distribution. The distribution parameters are estimated with time by neural networks using the history of TOBT updates. The main benefit of the proposed method is found in assigning the better pushback approval time of each departure aircraft for more efficient surface operations, which is demonstrated clearly by the simulation results. Using the proposed method, the aircraft operators can save fuel from improved ground operations via a probabilistic approach at the cost of reporting TOBT as a single point. © 2024 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
引用
收藏
页码:122 / 129
相关论文
共 50 条
  • [1] Off-block Time Prediction Using Operators' Prediction History
    Mori, Ryota
    2021 IEEE/AIAA 40TH DIGITAL AVIONICS SYSTEMS CONFERENCE (DASC), 2021,
  • [2] Impact of off-block time uncertainty on the control of airport surface operations
    Badrinath, Sandeep
    Balakrishnan, Hamsa
    Joback, Emily
    Reynolds, Tom G.
    Transportation Science, 2020, 54 (04): : 920 - 943
  • [3] Impact of Off-Block Time Uncertainty on the Control of Airport Surface Operations
    Badrinath, Sandeep
    Balakrishnan, Hamsa
    Joback, Emily
    Reynolds, Tom G.
    TRANSPORTATION SCIENCE, 2020, 54 (04) : 920 - 943
  • [4] Estimated Off-Block Time based on LSTM-TCN network
    Xing, Zhiwei
    Liu, Ke
    THIRD INTERNATIONAL CONFERENCE ON SENSORS AND INFORMATION TECHNOLOGY, ICSI 2023, 2023, 12699
  • [5] Aircraft Taxi Time Prediction Using Machine Learning and its Application for Departure Metering
    Kato, Furuto
    Itoh, Eri
    2023 IEEE/AIAA 42ND DIGITAL AVIONICS SYSTEMS CONFERENCE, DASC, 2023,
  • [6] Machine learning for predicting off-block delays: A case study at Paris - Charles de Gaulle International Airport
    Falque, Thibault
    Mazure, Bertrand
    Tabia, Karim
    DATA & KNOWLEDGE ENGINEERING, 2024, 152
  • [7] A reassessed model for mechanistic prediction of bubble departure and lift off diameters
    Mazzocco, T.
    Ambrosini, W.
    Kommajosyula, R.
    Baglietto, E.
    INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2018, 117 : 119 - 124
  • [8] Flight Departure Time Prediction Based on Deep Learning
    Zhou, Hang
    Li, Weicong
    Jiang, Ziqi
    Cai, Fanger
    Xue, Yuting
    AEROSPACE, 2022, 9 (07)
  • [9] Real-Time Travel Time Prediction Framework for Departure Time and Route Advice
    Calvert, Simeon C.
    Snelder, Maaike
    Bakri, Taoufik
    Heijligers, Bjorn
    Knoop, Victor L.
    TRANSPORTATION RESEARCH RECORD, 2015, (2490) : 56 - 64
  • [10] A robust optimization approach for airport departure metering under uncertain taxi-out time predictions
    Rocha Murca, Mayara Conde
    AEROSPACE SCIENCE AND TECHNOLOGY, 2017, 68 : 269 - 277