A novel self-organizing constructive neural network for estimating aircraft trip fuel consumption

Waqar Ahmed Khan, Sai-Ho Chung, Hoi-Lam Ma, Shi Qiang Liu and Ching Yuen Chan

Transportation Research Part E: Logistics and Transportation Review, 2019, vol. 132, issue C, 72-96

Abstract: Accurate estimation of aircraft fuel consumption is critical for airlines in terms of safety and profitability. In current practice, estimation of fuel consumption for a flight trip is usually done by engineering approaches, which mainly consider physical factors, e.g., planned weather and planned cruise level. However, the actual performance of a flight usually deviates from such estimation. Therefore, we propose a novel self-organizing constructive neural network (CNN) that features a cascade architecture and analytically determines connection weights to estimate the trip fuel of a flight. The proposed method generates non-redundant and linearly independent hidden units by an orthogonal linear transformation of operational parameters to achieve the best least-squares solution. Our findings provide insights for the aviation industry in controlling airlines’ excess fuel consumption.

Keywords: Aircraft fuel estimation; Engineering approach; High dimensional data; Machine learning; Neural network (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.tre.2019.10.005

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