A Data-Light and Trajectory-Based Machine Learning Approach for the Online Prediction of Flight Time of Arrival
Abstract
:1. Introduction
1.1. Literature Review
1.2. Contributions of the Paper
1.3. Organization of the Paper
2. Methodology
2.1. Definition
2.2. Overall Framework for ETA_TAB and ELDT Prediction
2.3. Trajectory Point Reconstruction and Matching
2.3.1. Trajectory Point Reconstruction
2.3.2. Trajectory Matching
2.4. Trajectory Prediction
2.4.1. LSTM Neural Network
2.4.2. LSTM Network for Trajectory Prediction
2.5. Ground Speed Prediction
Algorithm1: GBM Algorithm | |
1. | |
2. | do: |
3. | |
4. | |
5. | |
6. | End for |
2.6. ETA_TAB and ELDT Prediction
3. Demonstration of the Prediction Approach
3.1. Data
3.2. Trajectory Matching
3.3. Trajectory Prediction
3.4. Ground Speed Prediction
3.5. ETA_TAB and ELDT Prediction
3.5.1. ETA_TAB Prediction
3.5.2. ELDT Prediction
Sampling Terminal Approach Time
Results
4. Summary and Further Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. Ground Speeds of FLT1 and FLT2
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Airport Pair | Current Flight | Current Position | Best-Matched Historical Flight | Similarity Score | Matching Time (min) |
---|---|---|---|---|---|
DEN-SFO | FLT1 (UA1497(4/20/20)) | 25th GCD percentile | UA1497 (4/19/20) | 1.00 | 0.01 |
50th GCD percentile | UA1497 (4/19/20) | 1.00 | 0.04 | ||
75th GCD percentile | UA1497 (4/19/20) | 1.00 | 0.09 | ||
ORD-SFO | FLT2 (UA2166(5/9/20)) | 25th GCD percentile | UA2166 (3/3/20) | 1.00 | 0.28 |
50th GCD percentile | UA2166 (1/19/20) | 1.00 | 0.66 | ||
75th GCD percentile | UA2166 (1/19/20) | 0.99 | 1.05 |
Methods | Features | Training Data | Validation Data | ||
---|---|---|---|---|---|
DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | ||
LSTM | Longitude (degree) | 1.04 × 10−4 | 3.22 × 10−4 | 2.52 × 10−4 | 4.09 × 10−4 |
Latitude (degree) | 2.60 × 10−5 | 6.86 × 10−5 | 1.36 × 10−5 | 4.43 × 10−4 | |
(nm) | 0.33 | 0.84 | 0.10 | 1.37 | |
RNN | Longitude (degree) | 4.18 × 10−3 | 5.11 × 10−3 | 1.42 × 10−3 | 7.06 × 10−3 |
Latitude (degree) | 6.31 × 10−3 | 4.10 × 10−3 | 3.06 × 10−3 | 3.93 × 10−3 | |
(nm) | 1.71 | 4.29 | 1.30 | 3.35 | |
GRU | Longitude (degree) | 2.87 × 10−4 | 7.68 × 10−4 | 2.93 × 10−4 | 8.14 × 10−4 |
Latitude (degree) | 5.29 × 10−5 | 3.46 × 10−4 | 2.12 × 10−4 | 7.09 × 10−4 | |
(nm) | 0.85 | 1.59 | 0.79 | 1.41 |
GBM | Random Forest | |||
---|---|---|---|---|
FLT1 | FLT2 | FLT1 | FLT2 | |
25th GCD percentile | 7.36 | 10.01 | 7.63 | 12.04 |
50th GCD percentile | 9.41 | 12.98 | 9.42 | 13.97 |
75th GCD percentile | 9.83 | 13.56 | 10.4 | 15.29 |
25th GCD Percentile | 50th GCD Percentile | 75th GCD Percentile | ||||
---|---|---|---|---|---|---|
DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | |
Proposed approach | −0.43 | −0.30 | 0.28 | 2.75 | −0.91 | 0.31 |
Best match | 6.07 | −27.83 | 1.62 | −12.63 | 7.97 | −12.63 |
Average | 11.17 | 12.54 | 11.17 | 12.54 | 11.17 | 12.54 |
25th GCD Percentile | 50th GCD Percentile | 75th GCD Percentile | ||||
---|---|---|---|---|---|---|
DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | |
Proposed approach | 1.54 | 2.02 | 1.36 | 1.97 | 1.03 | 1.79 |
Best match | 9.55 | 9.84 | 9.55 | 8.37 | 9.38 | 8.58 |
Average | 7.88 | 9.68 | 7.88 | 9.68 | 7.88 | 9.68 |
25th GCD Percentile | 50th GCD Percentile | 75th GCD Percentile | ||||
---|---|---|---|---|---|---|
DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | |
Proposed approach—LSTM | −0.98 | −3.74 | 0.04 | −0.79 | −0.83 | 1.27 |
Best match | 6.08 | −27.70 | −0.20 | −10.07 | 7.15 | −10.07 |
Average | 11.17 | 16.15 | 11.17 | 16.15 | 11.17 | 16.15 |
Proposed approach—RNN | −1.23 | −4.65 | 0.54 | −1.67 | −1.65 | 1.93 |
Proposed approach—GRU | −1.09 | −4.02 | 0.06 | −1.04 | −0.98 | 1.31 |
Based on en-route flight time from the flight plan | 0 | 7.00 | 0 | 7.00 | 0 | 7.00 |
Using the scheduled landing time from the flight plan | 2.10 | −3.00 | 2.10 | −3.00 | 2.10 | −3.00 |
25th GCD Percentile | 50th GCD Percentile | 75th GCD Percentile | ||||
---|---|---|---|---|---|---|
DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | DEN-SFO | ORD-SFO | |
Proposed approach-LSTM | 2.78 | 4.76 | 2.65 | 4.54 | 2.08 | 3.96 |
Best match | 9.23 | 15.04 | 10.68 | 21.36 | 8.69 | 16.54 |
Average | 7.46 | 9.09 | 7.46 | 9.09 | 7.46 | 9.09 |
Proposed approach-RNN | 3.62 | 6.01 | 3.95 | 5.65 | 2.97 | 5.64 |
Proposed approach-GRU | 3.21 | 5.14 | 3.32 | 5.02 | 2.41 | 4.72 |
Based on en-route flight time in the flight plan | 3.02 | 7.34 | 3.02 | 7.34 | 3.02 | 7.34 |
Using the scheduled landing time in the flight plan | 28.53 | 10.16 | 28.53 | 10.16 | 28.53 | 10.16 |
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Share and Cite
Zheng, Z.; Zou, B.; Wei, W.; Tian, W. A Data-Light and Trajectory-Based Machine Learning Approach for the Online Prediction of Flight Time of Arrival. Aerospace 2023, 10, 675. https://doi.org/10.3390/aerospace10080675
Zheng Z, Zou B, Wei W, Tian W. A Data-Light and Trajectory-Based Machine Learning Approach for the Online Prediction of Flight Time of Arrival. Aerospace. 2023; 10(8):675. https://doi.org/10.3390/aerospace10080675
Chicago/Turabian StyleZheng, Zhe, Bo Zou, Wenbin Wei, and Wen Tian. 2023. "A Data-Light and Trajectory-Based Machine Learning Approach for the Online Prediction of Flight Time of Arrival" Aerospace 10, no. 8: 675. https://doi.org/10.3390/aerospace10080675
APA StyleZheng, Z., Zou, B., Wei, W., & Tian, W. (2023). A Data-Light and Trajectory-Based Machine Learning Approach for the Online Prediction of Flight Time of Arrival. Aerospace, 10(8), 675. https://doi.org/10.3390/aerospace10080675