Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/115896
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Logistics and Maritime Studies | - |
| dc.contributor | Department of Aeronautical and Aviation Engineering | - |
| dc.contributor | Mainland Development Office | - |
| dc.creator | Zhang, L | - |
| dc.creator | Bian, L | - |
| dc.creator | Jiang, C | - |
| dc.creator | Wu, L | - |
| dc.date.accessioned | 2025-11-12T05:54:07Z | - |
| dc.date.available | 2025-11-12T05:54:07Z | - |
| dc.identifier.issn | 1366-5545 | - |
| dc.identifier.uri | http://hdl.handle.net/10397/115896 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Pergamon Press | en_US |
| dc.subject | ADS-B data mining | en_US |
| dc.subject | Aircraft fuel consumption | en_US |
| dc.subject | Carbon accounting | en_US |
| dc.subject | Monotonic interpolation | en_US |
| dc.subject | Spectral method | en_US |
| dc.title | A comprehensive framework for estimating aircraft fuel consumption based on flight trajectories | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 203 | - |
| dc.identifier.doi | 10.1016/j.tre.2025.104339 | - |
| dcterms.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. | - |
| dcterms.accessRights | embargoed access | en_US |
| dcterms.bibliographicCitation | Transportation research. Part E, Logistics and transportation review, Nov. 2025, v. 203, 104339 | - |
| dcterms.isPartOf | Transportation research. Part E, Logistics and transportation review | - |
| dcterms.issued | 2025-11 | - |
| dc.identifier.scopus | 2-s2.0-105012637404 | - |
| dc.identifier.eissn | 1878-5794 | - |
| dc.identifier.artn | 104339 | - |
| dc.description.validate | 202511 bcjz | - |
| dc.description.oa | Not applicable | en_US |
| dc.identifier.SubFormID | G000357/2025-08 | en_US |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | This work was supported by the National Natural Science Foundation of China [Grant Nos. 72301230, 72371074; and additional support], the Shenzhen Science and Technology Program, China [Grant No. JCYJ20240813162012016], the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant No. 25223223], the Natural Science Foundation of Beijing Municipality [No. IS23129], the Research Centre for Low-Altitude Economy (RCLAE), the Hong Kong Polytechnic University [P0058168], and Beijing Wuzi University Youth Fund [2023XJQN19]. All financial supports are gratefully acknowledged. | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.date.embargo | 2028-11-30 | en_US |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
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