Abstract

The maintenance and improvement of safety are among the most critical concerns in civil aviation operations. Due to the increased availability of data and improvements in computing power, applying artificial intelligence technologies to reduce risk in aviation safety has gained momentum. In this paper, a framework is developed to build a predictive model of future aircraft trajectory that can be utilized online to assist air crews in their decision-making during approach. Flight data parameters from the approach phase between certain approach altitudes (also called gates) are utilized for training an offline model that predicts the aircraft’s ground speed at future points. This model is developed by combining convolutional neural networks (CNNs) and long short-term memory (LSTM) layers. Due to the myriad of model combinations possible, hyperband algorithm is used to automate the hyperparameter tuning process to choose the best possible model. The validated offline model can then be used to predict the aircraft’s future states and provide decision-support to air crews. The method is demonstrated using publicly available Flight Operations Quality Assurance (FOQA) data from the National Aeronautics and Space Administration (NASA). The developed model can predict the ground speed at an accuracy between 1.27% and 2.69% relative root-mean-square error. A safety score is also evaluated considering the upper and lower bounds of variation observed within the available data set. Thus, the developed model represents an improved performance over existing techniques in literature and shows significant promise for decision-support in aviation operations.

References

References
1.
Anon
,
2017
, “
Statistical Summary of Commercial Jet Airplane Accidents - Boeing Commercial Airplanes
,” http://www.boeing.com/resources/boeingdotcom/company/about˙bca/pdf/statsum.pdf,
Retrieved Oct. 2020
.
2.
Anon
,
2017
, “
Federal Aviaition Administration Aerospace Forecasts Fiscal Years 2016–2036
,” https://www.faa.gov/data˙research/aviation/aerospace˙forecasts/media/FY2016-36˙FAA˙Aerospace˙Forecast.pdf,
Retrieved Oct. 2020
.
3.
Zhang
,
X.
, and
Mahadevan
,
S.
,
2020
, “
Bayesian Neural Networks for Flight Trajectory Prediction and Safety Assessment
,”
Decision Support Syst.
,
131
(
4
), p.
113246
. 10.1016/j.dss.2020.113246
4.
Anon
,
2003
,
Federal Aviation Administration Advisory Circular 120–71a
.
Advisory Circular. Retrieved Oct. 2019
.
5.
Puranik
,
T.
,
Jimenez
,
H.
, and
Mavris
,
D.
,
2017
, “
Energy-Based Metrics for Safety Analysis of General Aviation Operations
,”
J. Aircraft
,
54
(
6
), pp.
2285
2297
. 10.2514/1.C034196
6.
Campbell
,
A.
,
Zaal
,
P.
,
Schroeder
,
J. A.
, and
Shah
,
S.
,
2018
, “
Development of Possible Go-around Criteria for Transport Aircraft
,”
2018 Aviation Technology, Integration, and Operations Conference
.
Paper No. AIAA-2018-3198
.
7.
Hegde
,
J.
, and
Rokseth
,
B.
,
2020
, “
Applications of Machine Learning Methods for Engineering Risk Assessment—A Review
,”
Safety Sci.
,
122
(
2
), p.
104492
. 10.1016/j.ssci.2019.09.015
8.
Anon
,
2011
, “
Federal Aviation Administration, 14 CFR Sec. 121.344 Digital Flight Data Recorders for Transport Category Airplanes
,” https://www.ecfr.gov/cgi-bin/text-idx?SID=b42b5be68aa3c7da5b85e2c60277e054&mc=true&node=se14.3.121_1344&rgn=div8.
Retrieved Oct. 2020
.
9.
Campbell
,
N.
,
2003
, “
Flight Data Analysis-an Airline Perspective
,”
The Australian Society of Air Safety Investigators (ASASI)
,
Maroochydore, Australia
.
10.
Logan
,
T. J.
,
2008
, “
Error Prevention As Developed in Airlines
,”
Int. J. Radiation Oncology Biol. Phys.
,
71
(
1
), pp.
S178
S181
. 10.1016/j.ijrobp.2007.09.040
11.
Martınez
,
D.
,
Fernández
,
A.
,
Hernández
,
P.
,
Cristóbal
,
S.
,
Schwaiger
,
F.
,
Nunez
,
J. M.
, and
Ruiz
,
J. M.
,
2019
, “
Forecasting Unstable Approaches With Boosting Frameworks and LSTM Networks
,”
9th SESAR Innovation Days
,
Athens, Greece
.
12.
Lee
,
H.
,
Madar
,
S.
,
Sairam
,
S.
,
Puranik
,
T. G.
,
Payan
,
A. P.
,
Kirby
,
M.
,
Pinon
,
O. J.
, and
Mavris
,
D. N.
,
2020
, “
Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning
,”
Aerospace
,
7
(
6
), p.
73
. 10.3390/aerospace7060073
13.
Tong
,
C.
,
Yin
,
X.
,
Li
,
J.
,
Zhu
,
T.
,
Lv
,
R.
,
Sun
,
L.
, and
Rodrigues
,
J. J.
,
2018
, “
An Innovative Deep Architecture for Aircraft Hard Landing Prediction Based on Time-Series Sensor Data
,”
Appl. Soft. Comput.
,
73
(
12
), pp.
344
349
. 10.1016/j.asoc.2018.07.061
14.
Tong
,
C.
,
Yin
,
X.
,
Wang
,
S.
, and
Zheng
,
Z.
,
2018
, “
A Novel Deep Learning Method for Aircraft Landing Speed Prediction Based on Cloud-Based Sensor Data
,”
Fut. Generat. Comput. Syst.
,
88
(
11
), pp.
552
558
. 10.1016/j.future.2018.06.023
15.
Diallo
,
O. N.
,
2012
, “
A Predictive Aircraft Landing Speed Model Using Neural Network
,”
2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC)
,
Williamsburg, VA
.
16.
Ackley
,
J. L.
,
Puranik
,
T. G.
, and
Mavris
,
D.
,
2020
, “
A Supervised Learning Approach for Safety Event Precursor Identification in Commercial Aviation
,”
AIAA AVIATION 2020 FORUM
,
Virtual
.
17.
Bleu-Laine
,
M.-H.
,
Puranik
,
T. G.
,
Mavris
,
D. N.
, and
Matthews
,
B.
,
2021
, “
Predicting Adverse Events and Their Precursors in Aviation Using Multi-Class Multiple-Instance Learning
,”
AIAA Scitech 2021 Forum
,
Virtual
.
18.
LeCun
,
Y.
,
Bengio
,
Y.
, and
Hinton
,
G.
,
2015
, “
Deep Learning
,”
Nature
,
521
(
7553
), pp.
436
444
. 10.1038/nature14539
19.
Iverson
,
D. L.
,
2004
, “
Inductive System Health Monitoring
,”
Technical Report, National Aeronautics and Space Administration
. https://ntrs.nasa.gov/search.jsp?R=20040068062.
20.
Zhao
,
Z.
,
Chen
,
W.
,
Wu
,
X.
,
Chen
,
P. C.
, and
Liu
,
J.
,
2017
, “
Lstm Network: A Deep Learning Approach for Short-Term Traffic Forecast
,”
IET Intel. Transport Syst.
,
11
(
2
), pp.
68
75
. 10.1049/iet-its.2016.0208
21.
Zhang
,
H.
, and
Zhu
,
T.
,
2018
, “
Aircraft Hard Landing Prediction Using Lstm Neural Network
,”
Proceedings of the Second International Symposium on Computer Science and Intelligent Control
,
Stockholm, Sweden
,
ACM
, p.
28
.
22.
Puranik
,
T. G.
,
Rodriguez
,
N.
, and
Mavris
,
D. N.
,
2020
, “
Towards Online Prediction of Safety-critical Landing Metrics in Aviation Using Supervised Machine Learning
,”
Trans. Research Part C: Emer. Technol.
,
120
(
11
), p.
102819
. 10.1016/j.trc.2020.102819
23.
Pasindu
,
H.
,
Fwa
,
T.
, and
Ong
,
G. P.
,
2011
, “
Computation of Aircraft Braking Distances
,”
Trans. Res. Record
,
2214
(
1
), pp.
126
135
. 10.3141/2214-16
24.
Wahi
,
M. K.
,
1979
, “
Airplane Brake-energy Analysis and Stopping Performance Simulation
,”
J. Aircraft
,
16
(
10
), pp.
688
694
. 10.2514/3.58590
25.
Goerzen
,
C.
,
Kong
,
Z.
, and
Mettler
,
B.
,
2010
, “
A Survey of Motion Planning Algorithms From the Perspective of Autonomous Uav Guidance
,”
J. Int. Robot. Syst.
,
57
(
1–4
), p.
65
. 10.1007/s10846-009-9383-1
26.
Musialek
,
B.
,
Munafo
,
C. F.
,
Ryan
,
H.
, and
Paglione
,
M.
,
2010
, “
Literature Survey of Trajectory Predictor Technology
,”
Federal Aviation Administration, William J. Hughes Technical Center, Technical Report
, pp.
1
31
.
27.
Wu
,
H.
,
Chen
,
Z.
,
Sun
,
W.
,
Zheng
,
B.
, and
Wang
,
W.
,
2017
, “
Modeling Trajectories With Recurrent Neural Networks
,”
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
,
Melbourne, Australia
, pp.
3083
3090
.
28.
Ayhan
,
S.
, and
Samet
,
H.
,
2016
, “
Aircraft Trajectory Prediction Made Easy With Predictive Analytics
,”
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
,
San Francisco, CA
, pp.
21
30
.
29.
Che
,
C.
,
Wang
,
H.
,
Fu
,
Q.
, and
Ni
,
X.
,
2019
, “
Combining Multiple Deep Learning Algorithms for Prognostic and Health Management of Aircraft
,”
Aerospace Sci. Technol.
,
94
(
11
), p.
105423
. 10.1016/j.ast.2019.105423
30.
Shi
,
Z.
,
Xu
,
M.
,
Pan
,
Q.
,
Yan
,
B.
, and
Zhang
,
H.
,
2018
, “
LSTM-Based Flight Trajectory Prediction
,”
2018 International Joint Conference on Neural Networks (IJCNN)
,
Rio de Janeiro, Brazil
,
IEEE
, pp.
1
8
.
31.
Wu
,
H.
,
Chen
,
Z.
,
Sun
,
W.
,
Zheng
,
B.
, and
Wang
,
W.
,
2017
, “
Modeling Trajectories With Recurrent Neural Networks
,”
Proceedings of the 26th International Joint Conference on Artificial Intelligence
,
Melbourne, Australia
.
32.
Puranik
,
T.
, and
Mavris
,
D.
,
2018
, “
Anomaly Detection in General-Aviation Operations Using Energy Metrics and Flight-Data Records
,”
J. Aerospace Inform. Syst.
,
15
(
1
), pp.
22
35
. 10.2514/1.I010582
33.
Puranik
,
T. G.
,
Harrison
,
E.
,
Min
,
S.
,
Jimenez
,
H.
, and
Mavris
,
D. N.
,
2016
, “
General Aviation Approach and Landing Analysis Using Flight Data Records
,”
16th AIAA Aviation Technology, Integration, and Operations Conference
,
Washington DC
, p.
3913
.
34.
Guyon
,
I.
, and
Elisseeff
,
A.
,
2003
, “
An Introduction to Variable and Feature Selection
,”
J. Mach. Learn. Res.
,
3
(
Mar
), pp.
1157
1182
.
35.
Sola
,
J.
, and
Sevilla
,
J.
,
1997
, “
Importance of Input Data Normalization for the Application of Neural Networks to Complex Industrial Problems
,”
IEEE. Trans. Nucl. Sci.
,
44
(
3
), pp.
1464
1468
. 10.1109/23.589532
36.
Reason
,
J.
,
2000
, “
Safety Paradoxes and Safety Culture
,”
Injury Control Safety Promotion
,
7
(
1
), pp.
3
14
. 10.1076/1566-0974(200003)7:1;1-V;FT003
37.
Kang
,
Z.
,
Shang
,
J.
,
Feng
,
Y.
,
Zheng
,
L.
,
Liu
,
D.
,
Qiang
,
B.
, and
Wei
,
R.
,
2020
, “
A Deep Sequence-to-sequence Method for Aircraft Landing Speed Prediction Based on Qar Data
,”
International Conference on Web Information Systems Engineering
,
Amsterdam and Leiden, Netherlands
, pp.
516
530
.
38.
Hochreiter
,
S.
, and
Schmidhuber
,
J.
,
1997
, “
Long Short-term Memory
,”
Neur. Comput.
,
9
(
8
), pp.
1735
1780
. 10.1162/neco.1997.9.8.1735
39.
Indolia
,
S.
,
Goswami
,
A. K.
,
Mishra
,
S.
, and
Asopa
,
P.
,
2018
, “
Conceptual Understanding of Convolutional Neural Network—A Deep Learning Approach
,”
Proc. Comput. Sci.
,
132
, pp.
679
688
. 10.1016/j.procs.2018.05.069
40.
Bergstra
,
J.
, and
Bengio
,
Y.
,
2012
, “
Random Search for Hyper-Parameter Optimization
,”
J. Mach. Learn. Res.
,
13
(
1
), pp.
281
305
.
41.
Snoek
,
J.
,
Larochelle
,
H.
, and
Adams
,
R.
,
2012
, “
Practical Bayesian Optimization of Machine Learning Algorithms
,”
Adv. Neur. Inform. Process. Syst.
,
4
(
6
), pp.
2951
2959
.
42.
Wang
,
Z.
,
Hutter
,
F.
,
Zoghi
,
M.
,
Matheson
,
D.
, and
de Feitas
,
N.
,
2016
, “
Bayesian Optimization in a Billion Dimensions Via Random Embeddings
,”
J. Art. Intel. Res.
,
55
, pp.
361
387
. 10.1613/jair.4806
43.
Li
,
L.
,
Jamieson
,
K.
,
DeSalvo
,
G.
,
Rostamizadeh
,
A.
, and
Talwalkar
,
A.
,
2017
, “
Hyperband: A Novel Bandit-based Approach to Hyperparameter Optimization
,”
J. Mach. Learn. Res.
,
18
(
1
), pp.
6765
6816
.
You do not currently have access to this content.