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.
Original language | English |
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Pages (from-to) | 72-96 |
Number of pages | 25 |
Journal | Transportation Research Part E: Logistics and Transportation Review |
Volume | 132 |
DOIs | |
Publication status | Published - Dec 2019 |
Keywords
- Aircraft fuel estimation
- Engineering approach
- High dimensional data
- Machine learning
- Neural network
ASJC Scopus subject areas
- Business and International Management
- Civil and Structural Engineering
- Transportation