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Submitted:
20 December 2023
Posted:
21 December 2023
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Ref | Year | Objective | Problems Addressed |
[25] | 2020 | stochastic modeling applications in aviation | demand and capacity management |
management of air traffic congestion | |||
[26] | 2022 | employment of AI techniques for improvement of ATM capability |
history of AI techniques |
structure of AI techniques | |||
advantages of AI methods | |||
applications to several representative ATM tasks | |||
[27] | 2022 | applications of Deep Reinforcement Learning (DRL) in conflict resolution | basics of conflict resolution |
construction of DRL | |||
practical demonstration of DRL in conflict resolution | |||
[28] | 2023 | Deep Learning (DL) applications for Air Traffic Management (ATM) | solutions are categorized based on DL techniques |
future recommendations based on the ATM solutions | |||
open challenges identified for DL applications and ATM solutions | |||
[29] | 2022 | benefits of AI within aviation/ATM domain | Working of general and ATM eXplainable Artificial Intelligence (XAI) |
exploring need of XAI | |||
existing solutions and their limitations | |||
formulate the findings into a conceptual framework | |||
[30] | 2020 | reviews 4D track prediction technology |
classification of existing prediction techniques |
combination methods with aircraft track data | |||
techniques of data mining used | |||
applicable scope of each method | |||
[31] | 2021 | survey of aircraft tracking systems | categorize existing techniques according to their approaches |
development of real-time DL-based Aircraft Tracking system | |||
[32] | 2020 | review of existing Conflict Resolution methods for manned and unmanned aircrafts | taxonomy to categories CR algorithms |
working of tactical and distributed framework | |||
overview of four CR algorithms | |||
testing of manned and unmanned scenarios |
Safety | Prediction | of | Air Traffic | Control |
conflict-free | forecast | aviation | manage | |
collision less | envisage | aircraft | administer | |
non-contention | detect | flight | governance |
Safe |
Predict | of | Air Traffic | Control |
Safe | Predict | Air Traffic | Control | |
Safe* | Predict* | Air Traffic | Control |
Safe | Predict | Air Traffic | Control |
safe | forecast | Air Traffic | control |
safe | envisage | Air Traffic | control |
safe | detect | Air Traffic | control |
conflict | predict | Air Traffic | control |
conflict | forecast | Air Traffic | control |
conflict | envisage | Air Traffic | control |
conflict | detect | Air Traffic | control |
….. | ….. | ….. | ….. |
….. | ….. | ….. | ….. |
contention | detect | flight | detection |
Year | Strings | Database | Pages Explored | Available Articles | Related Articles | Not Related Articles | Duplicate Articles | Total |
2023 | Safety Air Traffic Control | IEEE | 3 | 30 | 30 | 0 | 0 | 149 |
Elsevier | 3 | 30 | 30 | 0 | 0 | |||
Springer | 3 | 30 | 291 Book | 0 | 0 | |||
ACM | 3 | 30 | 30 | 0 | 0 | |||
mdpi | 3 | 30 | 30 | 0 | 0 | |||
Conflict Air Traffic Control | IEEE | 3 | 30 | 28 | 0 | 2 | 93 | |
Elsevier | 3 | 30 | 17 | 0 | 13 | |||
Springer | 3 | 30 | 13 | 0 | 17 | |||
ACM | 2 | 16 | 12 | 0 | 4 | |||
mdpi | 3 | 30 | 23 | 0 | 7 | |||
RiskAir Traffic Control | IEEE | 3 | 30 | 15 | 0 | 15 | 97 | |
Elsevier | 3 | 30 | 23 | 0 | 7 | |||
Springer | 3 | 30 | 19 | 0 | 101 Book | |||
ACM | 3 | 24 | 12 | 0 | 12 | |||
mdpi | 3 | 30 | 28 | 0 | 2 | |||
Contention Air Traffic Control | IEEE | 2 | 15 | 10 | 0 | 5 | 36 | |
Elsevier | 1 | 10 | 6 | 0 | 4 | |||
Springer | 1 | 10 | 10 | 0 | 0 | |||
ACM | 1 | 2 | 2 | 0 | 0 | |||
mdpi | 1 | 10 | 8 | 0 | 2 | |||
Safety Aviation Control | IEEE | 3 | 30 | 19 | 0 | 11 | 83 | |
Elsevier | 3 | 30 | 20 | 0 | 10 | |||
Springer | 3 | 30 | 15 | 0 | 141 Book | |||
ACM | 2 | 13 | 7 | 0 | 6 | |||
mdpi | 3 | 30 | 22 | 0 | 8 | |||
Conflict Aviation Control | IEEE | 3 | 30 | 14 | 0 | 16 | 49 | |
Elsevier | 3 | 30 | 11 | 0 | 19 | |||
Springer | 3 | 30 | 8 | 0 | 22 | |||
ACM | 1 | 2 | 0 | 0 | 2 | |||
mdpi | 3 | 30 | 16 | 0 | 14 | |||
Risk Aviation Control | IEEE | 3 | 30 | 19 | 0 | 11 | 69 | |
Elsevier | 3 | 30 | 17 | 0 | 13 | |||
Springer | 3 | 30 | 14 | 0 | 151 Book | |||
ACM | 2 | 12 | 1 | 0 | 11 | |||
mdpi | 3 | 30 | 18 | 0 | 12 | |||
Contention Aviation Control | IEEE | 1 | 2 | 2 | 0 | 0 | 39 | |
Elsevier | 2 | 16 | 13 | 0 | 3 | |||
Springer | 3 | 25 | 20 | 0 | 5 | |||
ACM | 1 | 1 | 1 | 0 | 0 | |||
mdpi | 1 | 3 | 3 | 0 | 0 | |||
Safety Aircraft Control | IEEE | 3 | 30 | 12 | 0 | 18 | 62 | |
Elsevier | 3 | 30 | 15 | 0 | 15 | |||
Springer | 3 | 30 | 19 | 0 | 101 Book | |||
ACM | 2 | 19 | 7 | 0 | 12 | |||
mdpi | 3 | 30 | 9 | 0 | 21 | |||
Conflict Aircraft Control | IEEE | 3 | 30 | 12 | 0 | 18 | 44 | |
Elsevier | 3 | 30 | 5 | 0 | 25 | |||
Springer | 3 | 30 | 12 | 0 | 18 | |||
ACM | 1 | 3 | 0 | 0 | 3 | |||
mdpi | 3 | 30 | 15 | 0 | 15 | |||
RiskAircraft Control | IEEE | 3 | 30 | 13 | 0 | 17 | 40 | |
Elsevier | 3 | 30 | 7 | 0 | 23 | |||
Springer | 3 | 30 | 6 | 0 | 24 | |||
ACM | 3 | 22 | 6 | 0 | 16 | |||
mdpi | 3 | 30 | 8 | 0 | 22 | |||
ContentionAircraft Control | IEEE | 1 | 4 | 1 | 0 | 3 | 21 | |
Elsevier | 2 | 15 | 9 | 0 | 6 | |||
Springer | 3 | 30 | 22 | 0 | 71 Book | |||
ACM | 1 | 2 | 0 | 2 | ||||
mdpi | 1 | 6 | 3 | 3 | ||||
2022 | Safety PredictionAir Traffic Control | IEEE | 3 | 30 | 25 | 5 | 0 | 78 |
Elsevier | 3 | 30 | 7 | 23 | 0 | |||
Springer | 3 | 30 | 16 | 14 | 0 | |||
ACM | 2 | 15 | 4 | 11 | 0 | |||
mdpi | 3 | 30 | 26 | 4 | 0 | |||
Conflict predictionAir Traffic Control | IEEE | 3 | 30 | 15 | 5 | 10 | 41 | |
Elsevier | 3 | 30 | 14 | 12 | 4 | |||
Springer | 3 | 30 | 6 | 18 | 6 | |||
ACM | 1 | 3 | 0 | 3 | 0 | |||
mdpi | 3 | 30 | 6 | 10 | 14 | |||
2021 | Safety PredictionAir Traffic Control | IEEE | 3 | 30 | 20 | 10 | 0 | 94 |
Elsevier | 3 | 30 | 23 | 7 | 0 | |||
Springer | 3 | 30 | 23 | 7 | 1 | |||
ACM | 2 | 15 | 7 | 8 | 0 | |||
mdpi | 3 | 30 | 21 | 9 | 0 | |||
Conflict predictionAir Traffic Control | IEEE | 3 | 30 | 11 | 9 | 10 | 36 | |
Elsevier | 3 | 30 | 18 | 4 | 8 | |||
Springer | 3 | 30 | 5 | 18 | 7 | |||
ACM | 1 | 8 | 0 | 6 | 2 | |||
mdpi | 3 | 30 | 2 | 18 | 10 |
Ref | coordinate/ trajectory |
time-to-fly/ touch down |
Separation Minima | safety performance | controller/ pilot role |
data attacks | risk prediction | conflict prediction |
[1] | Y | |||||||
[2] | Y | |||||||
[3] | Y | |||||||
[4] | Y | |||||||
[5] | Y | Y | ||||||
[6] | Y | |||||||
[7] | Y | |||||||
[8] | Y | |||||||
[9] | Y | |||||||
[10] | Y | |||||||
[11] | Y | |||||||
[12] | Y | |||||||
[13] | Y | |||||||
[14] | Y | Y | ||||||
[15] | Y | |||||||
[16] | Y | Y | ||||||
[17] | Y | Y | ||||||
[18] | Y | |||||||
[19] | Y | |||||||
[20] | Y | |||||||
[21] | Y | |||||||
[22] | Y | Y | ||||||
[23] | Y | Y | ||||||
[24] | Y |
Database | Pre-Filtering Count (ZOTERO) | Title-based Filtering Count | Abstract-based Filtering Count | |||||||||
2020 | 2021 | 2022 | 2023 | 2020 | 2021 | 2022 | 2023 | 2020 | 2021 | 2022 | 2023 | |
IEEE | 22 | 25 | 45 | 178 | 5 | 31 | 40 | 18 | 2 | 2 | 5 | 2 |
Elsevier | 12 | 32 | 24 | 199 | 4 | 32 | 24 | 9 | 0 | 0 | 2 | 2 |
Springer | 11 | 29 | 21 | 182 | 3 | 29 | 21 | 13 | 0 | 0 | 1 | 1 |
ACM | 2 | 6 | 4 | 79 | 0 | 6 | 4 | 4 | 0 | 0 | 0 | 0 |
mdpi | 30 | 23 | 64 | 268 | 1 | 23 | 32 | 14 | 1 | 2 | 4 | 0 |
Total | 77 | 115 | 158 | 906 | 13 | 121 | 121 | 58 | 3 | 4 | 12 | 5 |
Ref | ANN | RNN | CNN | LSTM | QRF | RF | BI | DT | GB | C | R | MC | BM | LR | LS | GP | P | KB | KNN |
[1] | Y | Y | |||||||||||||||||
[2] | Y | Y | Y | ||||||||||||||||
[3] | Y | ||||||||||||||||||
[4] | Y | ||||||||||||||||||
[5] | Y | ||||||||||||||||||
[6] | Y | Y | Y | ||||||||||||||||
[7] | Y | ||||||||||||||||||
[8] | Y | Y | |||||||||||||||||
[9] | Y | Y | |||||||||||||||||
[10] | Y | ||||||||||||||||||
[11] | Y | ||||||||||||||||||
[12] | Y | ||||||||||||||||||
[13] | Y | ||||||||||||||||||
[14] | Y | ||||||||||||||||||
[15] | Y | Y | |||||||||||||||||
[16] | Y | ||||||||||||||||||
[17] | Y | Y | |||||||||||||||||
[18] | Y | ||||||||||||||||||
[19] | Y | ||||||||||||||||||
[20] | Y | ||||||||||||||||||
[21] | Y | ||||||||||||||||||
[22] | Y | Y | Y | ||||||||||||||||
[23] | Y | ||||||||||||||||||
[24] | Y |
Ref. | Year | Scheme | Strengths | Limitations |
[1] | 2022 | Inception | safe aircraft coordinate prediction scheme | ADS-B signal carries limited information |
LSTM | widens the perception field | CNN do not encode position and orientation | ||
CNN | time window based continuous time points | CNN needs a lot of training data to be effective | ||
CNN is accurate at image recognition and classification | CNNs tend to be much slower | |||
CNN offers Weight sharing | long training time for multi layer CNN | |||
CNN minimize computation via regular neural network | CNN recognize image as clusters of pixels | |||
CNNs uses same knowledge across all image locations | LSTM small context window size(input’s set) | |||
LSTM handles long-term dependencies effectively | LSTM do not deal big temporal dependencies | |||
LSTM is less susceptible to vanishing gradient problem | ||||
LSTM handles complex sequential data efficiently | ||||
LSTM NN make prediction more accurate | ||||
[2] | 2022 | LSTM | geometric and grouping-based conflicts detection | slow for mid and long-term trajectory prediction |
CNN | effective short-term trajectory predictions | ADS-B signal carries limited information | ||
LS | trajectory prediction accuracy’s impacts are discussed | less reliable neural network | ||
CNN is accurate at image recognition and classification | CNN do not encode position and orientation | |||
CNN offers Weight sharing | CNN needs a lot of training data to be effective | |||
CNN minimize computation via regular neural network | CNNs tend to be much slower | |||
CNNs uses same knowledge across all image locations | long training time for multi layer CNN | |||
LSTM handles long-term dependencies effectively | CNN recognize image as clusters of pixels | |||
LSTM is less susceptible to vanishing gradient problem | LSTM small context window size(input’s set) | |||
LSTM models complex sequential data efficiently | LSTM do not deal big temporal dependencies | |||
LSTM NN make prediction more accurate | ||||
[3] | 2021 | QRF | a tool for arrival time-to-fly prediction | slow for trade-off between two scenarios |
specific needs of the use case based prediction | considers aircraft present state and weather | |||
QRF gives non-parametric estimates of median and quantile predicted value | QRF not evaluated for variable length of time-series input | |||
QRF focus model uncertainty | ||||
[4] | 2021 | BI (R) | safety prediction for European airspace | only for European Airspace |
compatible with reduced data and expert’s opinions | considers only four parameters | |||
captures hierarchical dependencies between parameters | BI does not inform about prior selection | |||
BI has high statistical capacity in low probability events | BI makes posterior distributions largely influenced by priors | |||
BI combines prior information with data | BI gives high computational cost for models with large number of parameters | |||
BI convenient for hierarchical models and missing data | ||||
BI provides exact inferences for conditional on data | ||||
[5] | 2022 | LSTM | combines fault diagnosis and risk theory | lacks time-constrained dynamic risk analysis |
integrates real-time data and post-flight failure data | do not address correlation between failures | |||
verify the accuracy of the prediction | LSTM small context window size(input’s set) | |||
LSTM handles long-term dependencies effectively | LSTM do not deal big temporal dependencies | |||
LSTM is less susceptible to vanishing gradient problem | ||||
LSTM models complex sequential data efficiently | ||||
LSTM NN make prediction more accurate | ||||
[6] | 2022 | MC | simulation based real cases are considered | only considers influence of wind |
GP | MC provides multiple probability and outcomes from large pool of random data samples | Considers only two aircraft | ||
BM | MC offers clearer picture than a deterministic forecast | MC results are highly dependent on the input values and distribution | ||
SDE detects actual states in a model from data | MC take excessive computational powers | |||
SDE prediction remains close to data even when model parameters are incorrect | SDE may not have solutions that can be expressed in terms of elementary functions | |||
SDE requires substantial mathematical machinery to understand at any depth | ||||
[7] | 2022 | BI | keeps the number of adverse outputs low | only two high relevance variables |
varying input conditions | aircraft operation variables not considered | |||
BI’s high statistical capacity in low probability events | BI does not inform about prior selection | |||
BI combines prior information with data | BI makes posterior distributions largely influenced by priors | |||
BI convenient for hierarchical models and missing data | BI gives high computational cost for models with large number of parameters | |||
BI provides exact inferences for conditional on data | ||||
[8] | 2022 | BI | model dimension is decreased by increasing relevance and reducing redundancy | constructed for a particular airport |
RNN | a regression model to address class imbalance problem | runway configurations often vary | ||
gives information about severity of hard landing | data is used from only one aircraft type | |||
RNN is dynamic and computationally powerful | does not estimate reference speed | |||
RNN is capable of approximating arbitrary nonlinear dynamic systems with arbitrary precision | poor performance when data is not adequate | |||
RNN remembers each information through time | RNN has vanishing and exploding gradient | |||
LSTM NN make prediction more accurate | RNN training is difficult | |||
probabilistic NN training by Bayesian approach supports risk-informed decision making | RNN can not compute long sequences for tanh and relu activation functions | |||
BI has high statistical capacity in low probability events | BI does not inform about prior selection | |||
BI combines prior information with data | BI makes posterior distributions largely influenced by priors | |||
BI convenient for hierarchical models and missing data | BI gives high computational cost for models with large number of parameters | |||
BI provides exact inferences for conditional on data | ||||
[9] | 2022 | RNN | clarify key air traffic operations for safety monitoring | RNN has vanishing and exploding gradient |
CNN | RNN is dynamic and computationally powerful | RNN training is difficult | ||
RNN approximates arbitrary nonlinear dynamic systems having arbitrary precision | RNN can not compute long sequences for tanh and relu activation functions | |||
RNN remembers each information through time | CNN do not encode position and orientation | |||
CNN is accurate at image recognition and classification | CNN needs a lot of training data to be effective | |||
CNN offers Weight sharing | CNNs tend to be much slower | |||
CNN minimize computation via regular neural network | long training time for multi layer CNN | |||
CNNs uses same knowledge across all image locations | CNN recognize image as clusters of pixels | |||
[10] | 2022 | GP | less computation time. | estimation error increases during sharp turns |
accurate conflict probability as numerical approaches | lacks 3D multi-aircraft encounter | |||
can be easily applied to 3D scenarios | considers wind models | |||
GP directly captures the model uncertainty | GP are not sparse and uses complete information of features for prediction | |||
GP prior about model’s shape is provided through selection of different kernel functions | GP efficiency degrades in high dimensional spaces when number of features increases | |||
[11] | 2022 | BI | handles random variables and process uncertainties | considers only one airspace sector |
reduced computational efforts | BI does not inform about prior selection | |||
BI has high statistical capacity in low probability events | BI makes posterior distributions largely influenced by priors | |||
BI combines prior information with data | BI gives high computational cost for models with large number of parameters | |||
BI convenient for hierarchical models and missing data | ||||
BI provides exact inferences for conditional on data | ||||
[12] | 2022 | BI | identification of variables related to controllers performance | BI does not inform about prior selection |
BI has high statistical capacity in low probability events | BI makes posterior distributions largely influenced by priors | |||
BI combines prior information with data | BI gives high computational cost for models with large number of parameters | |||
BI convenient for hierarchical models and missing data | ||||
BI provides exact inferences for conditional on data | ||||
[13] | 2022 | LSTM | a digital conflict detection system is developed | predicts conflicts in sequential flight data |
large-scale and real time air traffic models are built | LSTM small context window size(input’s set) | |||
predicts four factors | LSTM do not deal big temporal dependencies | |||
LSTM handles long-term dependencies effectively | ||||
LSTM is less susceptible to vanishing gradient problem | ||||
LSTM models complex sequential data efficiently | ||||
LSTM NN make prediction more accurate | ||||
[14] | 2022 | GB | time dependence analysis | GB overemphasize outliers and cause overfitting |
learning assurance analyses performed | GB is computationally expensive | |||
GB often provides predictive accuracy | GB is less interpretative in nature | |||
GB has lots of flexibility | GB influence model behavior as it results in many parameters | |||
GB has no data preprocessing and handle missing data | ||||
[15] | 2022 | C | Four-Dimension Trajectory based conflict predictions | operation and environment variables not used |
R | C is effective in high dimensional spaces | other sources for 4DT predictions as input | ||
C is memory efficient | C slow real time prediction | |||
R uses more than two independent variables | C is difficult to implement | |||
R determines the unbiased relationship between two variables by controlling effects of other variables | C is based on complex algorithm | |||
R cannot work properly with poor quality data | ||||
R susceptible to collinear problems | ||||
[16] | 2023 | RF | RF has easy-to-understand hyperparameters | RF increased accuracy requires more trees |
RFclassifier doesn't overfit with enough trees | More RF trees slow down model | |||
RF an’t describe relationships within data | ||||
[17] | 2023 | DT | a large amount of data is handled efficiently | DT are unstable |
P | reduce the computational cost economically | DT predictions neither smooth nor continuous | ||
DT has simple interpretation and visualization | biased DTs are created if some classes dominate | |||
DT performs better even if its assumptions breach | P sometimes affects the accuracy negatively | |||
DT works well with numerical and categorical data also multi-output problems | P performance and accuracy depends on data’s nature | |||
DT is less costly | ||||
P reduces the size of decision trees | ||||
P is advantageous in removing the redundant rules | ||||
[18] | 2023 | LSTM | process airspace flight image frames | can locate anomalous targets |
integrates flight plan with ADS-B data | LSTM small context window size(input’s set) | |||
detection of ADS-B anomalous attack in ATC system | LSTM do not deal big temporal dependencies | |||
LSTM handles long-term dependencies effectively | ||||
LSTM is less susceptible to vanishing gradient problem | ||||
LSTM models complex sequential data efficiently | ||||
LSTM NN make prediction more accurate | ||||
[19] | 2023 | LSTM | A multi-factorial model and multi-modal system | small sample size |
An encoder-decoder LSTM network | task complexity design | |||
LSTM handles long-term dependencies effectively | studies conducted in simulated environment | |||
LSTM is less susceptible to vanishing gradient problem | LSTM small context window size(input’s set) | |||
LSTM models complex sequential data efficiently | LSTM do not deal big temporal dependencies | |||
LSTM NN make prediction more accurate | ||||
[20] | 2023 | BI | data-driven classification approach is provided with a hierarchical structure | regional incident data is used |
resource investment optimization with efficiency | BI does not inform about prior selection | |||
BI has high statistical capacity in low probability events | BI makes posterior distributions largely influenced by priors | |||
BI combines prior information with data | BI gives high computational cost for models with large number of parameters | |||
BI convenient for hierarchical models and missing data | ||||
BI provides exact inferences for conditional on data | ||||
[21] | 2023 | LSTM | LSTM handles long-term dependencies effectively | LSTM small context window size(input’s set) |
LSTM is less susceptible to vanishing gradient problem | LSTM do not deal big temporal dependencies | |||
LSTM is efficient at modeling complex sequential data | ||||
LSTM NN make prediction more accurate | ||||
[22] | 2020 | DT | DT has simple interpretation and visualization | over-complex DT do not generalize data well |
KB | DT performs better even if its assumptions breach | DT are unstable | ||
KNN | DT works well with numerical and categorical data also multi-output problems | DT predictions neither smooth nor continuous | ||
DT is less costly | biased DTs are created if some classes dominate | |||
KB is simple to Implement. | KB Conditional Independence Assumption do not always hold | |||
KB is very fast | KB has Zero probability problem | |||
KB gives accurate results if conditional Independence assumption holds | KNN does not perform better on large dataset | |||
KNN no Training Period and new data adds seamlessly | KNN does not perform better on high dimension space | |||
KNN is very easy to implement | KNN need feature scaling | |||
KNN sensitive to noisy, missing data and outlier | ||||
[23] | 2020 | LR | use historic traffic time series data in different periods | need to consider more factors |
LR is easier to set up and train | low performance | |||
LR good differ data outcome are linearly separable | LR fails to predict a continuous outcome | |||
LR may not be accurate for small sample size | ||||
LR assumes linearity between predicted and predictor variables | ||||
[24] | 2020 | ANN | NN is predictive model and automatic learning feature | NN generates a certain output is questionable |
NN handle large, complex, missing and sequential data | NN is complicated and long development time | |||
NN exhibits improved performance | NN usually require much more data | |||
NN handling structured and unstructured data and also non-linear relationships | NN are more computationally expensive | |||
NN offers scalability and generalization |
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