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A comprehensive framework for estimating aircraft fuel consumption based on flight trajectories

Author

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  • Zhang, Linfeng
  • Bian, Lei
  • Jiang, Changmin
  • Wu, Lingxiao

Abstract

The calculation of aircraft fuel consumption is crucial for airline operations management, environmental protection, and other aspects. The use of Automatic Dependent Surveillance-Broadcast (ADS-B) data can significantly enhance the accuracy and resolution of fuel consumption calculations over flight routes. However, ADS-B data exhibits several issues, including inconsistent data point distribution, uneven interval widths, and variability in flight durations, making it difficult to align with fuel consumption data. This study developed a comprehensive mathematical framework and established a connection between flight dynamics in ADS-B data and fuel consumption, providing a set of high-precision, high-resolution fuel calculation methods. It also allows other practitioners to select data sources according to specific needs through this framework. The framework includes three main steps: (1) Using ADS-B data to determine the flight profile, which involves identifying aircraft behaviors such as acceleration, deceleration, climb, and descent, and establishing their theoretical relationships and parameters with fuel consumption. (2) Fitting the coefficients of the relationship between flight profiles and fuel consumption using available interval fuel consumption data. (3) Calculating instantaneous fuel consumption through monotonic and smooth interpolation. Our comprehensive analysis of ADS-B and Aircraft Communications Addressing and Reporting System (ACARS) datasets from April 2022 to December 2023, focusing on interval and instantaneous fuel consumption patterns among China’s 11 most prevalent aircraft types. The interval fuel consumption error demonstrates a reduction to 0.1% at the 10th percentile of the error distribution, while maintaining a mean error of 3.31%. Even at the 90th percentile, the error remains below 10%, demonstrating the model’s robustness across most scenarios. Regarding instantaneous fuel consumption model performance, the Boeing 737 MAX 800 demonstrates a cumulative instantaneous fuel consumption error of just 6.6% during cruise phase, while the Airbus A321-200 shows errors of 7.1% during climb and 8.4% during descent phases.

Suggested Citation

  • Zhang, Linfeng & Bian, Lei & Jiang, Changmin & Wu, Lingxiao, 2025. "A comprehensive framework for estimating aircraft fuel consumption based on flight trajectories," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:transe:v:203:y:2025:i:c:s1366554525003801
    DOI: 10.1016/j.tre.2025.104339
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    1. Francisco Velásquez-SanMartín & Xabier Insausti & Marta Zárraga-Rodríguez & Jesús Gutiérrez-Gutiérrez, 2021. "A Mathematical Model for the Analysis of Jet Engine Fuel Consumption during Aircraft Cruise," Energies, MDPI, vol. 14(12), pages 1-13, June.
    2. Ekici, Selcuk & Ayar, Murat & Kilic, Ugur & Karakoc, T. Hikmet, 2023. "Performance based analysis for the Ankara-London route in terms of emissions and fuel consumption of different combinations of aircraft/engine: An IMPACT application," Journal of Air Transport Management, Elsevier, vol. 108(C).
    3. Lyu, Chen & Liu, Xiaoman & Wang, Zhen & Yang, Lu & Liu, Hao & Yang, Nan & Xu, Shaodong & Cao, Libin & Zhang, Zhe & Pang, Lingyun & Zhang, Li & Cai, Bofeng, 2023. "An emissions inventory using flight information reveals the long-term changes of aviation CO2 emissions in China," Energy, Elsevier, vol. 262(PB).
    4. Lei Kang & Mark Hansen, 2021. "Quantile Regression–Based Estimation of Dynamic Statistical Contingency Fuel," Transportation Science, INFORMS, vol. 55(1), pages 257-273, 1-2.
    5. Simone Speizer & Jay Fuhrman & Laura Aldrete Lopez & Mel George & Page Kyle & Seth Monteith & Haewon McJeon, 2024. "Integrated assessment modeling of a zero-emissions global transportation sector," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    6. Ming Zhang & Qianwen Huang & Sihan Liu & Yu Zhang, 2019. "Fuel Consumption Model of the Climbing Phase of Departure Aircraft Based on Flight Data Analysis," Sustainability, MDPI, vol. 11(16), pages 1-23, August.
    7. Chen, Shuiwang & Wu, Lingxiao & Ng, Kam K.H. & Liu, Wei & Wang, Kun, 2024. "How airports enhance the environmental sustainability of operations: A critical review from the perspective of Operations Research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    8. Patel, Vivek & Savsani, Vimal & Mudgal, Anurag, 2018. "Efficiency, thrust, and fuel consumption optimization of a subsonic/sonic turbojet engine," Energy, Elsevier, vol. 144(C), pages 992-1002.
    9. Elena Fernández & Markus Leitner & Ivana Ljubić & Mario Ruthmair, 2022. "Arc Routing with Electric Vehicles: Dynamic Charging and Speed-Dependent Energy Consumption," Transportation Science, INFORMS, vol. 56(5), pages 1219-1237, September.
    10. Lavanya Marla & Bo Vaaben & Cynthia Barnhart, 2017. "Integrated Disruption Management and Flight Planning to Trade Off Delays and Fuel Burn," Transportation Science, INFORMS, vol. 51(1), pages 88-111, February.
    11. Brueckner, Jan K. & Kahn, Matthew E. & Nickelsburg, Jerry, 2024. "How do airlines cut fuel usage, reducing their carbon emissions?," Economics of Transportation, Elsevier, vol. 38(C).
    12. 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).
    13. Babikian, Raffi & Lukachko, Stephen P. & Waitz, Ian A., 2002. "The historical fuel efficiency characteristics of regional aircraft from technological, operational, and cost perspectives," Journal of Air Transport Management, Elsevier, vol. 8(6), pages 389-400.
    14. Wang, Lei & Wu, Changxu & Sun, Ruishan, 2014. "An analysis of flight Quick Access Recorder (QAR) data and its applications in preventing landing incidents," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 86-96.
    15. 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.
    16. Huang, Chenyu & Cheng, Xiaoyue, 2022. "Estimation of aircraft fuel consumption by modeling flight data from avionics systems," Journal of Air Transport Management, Elsevier, vol. 99(C).
    17. James C. Jones & David J. Lovell & Michael O. Ball, 2018. "Stochastic Optimization Models for Transferring Delay Along Flight Trajectories to Reduce Fuel Usage," Transportation Science, INFORMS, vol. 52(1), pages 134-149, January.
    18. Yiming Liu & Yang Yu & Yu Zhang & Roberto Baldacci & Jiafu Tang & Xinggang Luo & Wei Sun, 2023. "Branch-Cut-and-Price for the Time-Dependent Green Vehicle Routing Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 14-30, January.
    19. Xinhua Zhu & Nan Li & Yu Sun & Hongfei Zhang & Kai Wang & Sang-Bing Tsai, 2018. "A Study on the Strategy for Departure Aircraft Pushback Control from the Perspective of Reducing Carbon Emissions," Energies, MDPI, vol. 11(9), pages 1-15, September.
    20. Liu, Yulin & Hansen, Mark & Ball, Michael O. & Lovell, David J., 2021. "Causal analysis of flight en route inefficiency," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 91-115.
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