Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

<jats:title>Abstract</jats:title>         <jats:p>Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.</jats:p>

section_content: Moreover, the traffic or pattern areas in nearby airports may be dependent or independent, and each airport may have several parallel or non-parallel runways. In this regard, the traffic of parallel runways may be dependent or independent, whereas crossover runways' traffic is dependent. Landing and takeoff runways might differ in every airport, or they might be jointly utilized (3, 4). Furthermore, each runway may have several landing and takeoff procedures, which might have dependent and independent traffic. 

These issues demonstrate the high complexity of the problem modeling. 

Considering the huge scale of air traffic data (large data amount) in the classification learning process, the complexity level is higher with the increased number of categories of each class. Additionally, the selection of the significant features by traditional data mining approaches is almost impossible. 

Various measures have been taken to solve the problems of ATM and ASP (5). Many studies have aimed to solve these issues using mathematical models and methods, linear programming, mixed planning, and statistical models. 

However, one of the limitations of these studies is not considering the actual data and operating environment. 

Therefore, the proposed solutions lack the required accuracy and efficiency in the actual environments of airport aviation operations (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17). Some scholars have attempted to address the issue of traffic control and delay only by considering climatic and environmental conditions (18) (19) (20) (21). 

Other studies have used the first come first serve (FCFS) technique, along with the queue model and other mixed methods, to solve the problem (22) (23) (24) (25) (26). 

Another category of research and articles have applied data mining methods to investigate the influential factors in air traffic and flights (27) (28) (29) (30) (31) (32). 

The machine learning algorithm is a conventional method which used to resolve the issues of air traffic, ASL, delay forecast, and minimization. 

Overall, this technique has shown better problem-solving efficiency compared to other methods (33) (34) (35) (36) (37) (38). Some studies have also supported the uncertainty and fuzzy states, incorporating the latter into other techniques (39) (40) (41). Such solutions have been proposed for AT problems and are based on the Internet of Things (IoT) (42) (43) (44), optimization methods (45) (46) (47) (48), multi-objective optimization techniques (49) (50) (51), and intelligent agent-based methods (52). 

An optimal approach to solving the mentioned issue involves using the structure of artificial neural networks (53) (54) (55). Following the evolution of neural networks, deep learning These anomalies which might be an unusual sequence of events or an unusual period between events. The second type of data is aviation crash reports, which have no strict rules, and the pilot needs no specific conditions as the reports include narrations.

section_name: Deep learning method

section_name: Artificial neural network based methods

section_name: Bayesian network based methods

section_name: Multi goal methods

section_content: However, a method should be designed to determine the significant data due to their lack of unity. 

As for labels and labeled data in Meanwhile, the queue model and recursive neural network were observed to have higher learning levels. 

In another study, a 3-D convolutional neural network (R-3D CNN) was applied to increase the accuracy of air traffic predictive accuracy (63). 

Changes in spatial-temporal air movements could be comprehensively considered by using this algorithm. In
section_name: Single goal methods

section_name: 3.

section_content: The proposed model in the present study was based on the LSTM and ELM algorithms. were an entirely connected layer, the layer's input was a vector, and the output was an actual number. In Figure 3, the initial structure of the LSTM cell (1) has been demonstrated, which is interpreted as follows:

BiLSTMs in neural networks (68). In addition, theoretical evidence suggests that a deep hierarchical model is more efficient in delivering some functions than the shallow type. In the present study, an accumulated BiLSTM network was defined, where the output y t from the lower layer was converted into the input of the upper layer. The accumulated BiLSTM structure is shown in the following figure(4): The definition of A=(a 1,a 2,…,a m ) and Q=(q 1,q 2,…q m ) show the sequence of the problem and sequence of the responses, respectively, where n and m demonstrate the length of the problem and responses, a t and q t are the the t-th words of the problem and responses. In this section, the stacked BiLSTM was implemented on the problem and responses, and the hidden-mode H Q and H A matrices were obtained. h q t = sBiLSTM( h q t-1, h q t+1, qt), h q 0 = 0, h a t = sBiLSTM( h a t-1, h a t+1, at), h a 0 =h q n, The calculations are as follows:

where d represents the dimensions of the hidden mode.
section_name: Proposed Method

section_name: Coherence Mechanism for

section_content: In this section, a coherence mechanism was implemented to encode the problem in accordance with the response sequence (Figure 5 ). We

In order to obtain the accuracy vector of the question with respect to each word of the response, we combined the explanatory weights and approximation matrix to calculate the new CQ and CA field vectors. In this section, C Q and C A were the results of the interaction between the problem and vector response, as follows: function, and the ELM model could be defined, as follows:

The matrix was equal to:
section_name: Problem Presentation

section_content: In the equation above

The inverse H + was generalized from the hidden output layer matrix. In the final step, we examined the assessable criteria.
section_name: Hβ=Y

section_content: At this stage of the research, we are analyzed and evaluated the applied data and assessed the results and criteria.
section_name: Analysis and Evaluation

section_content: The dataset obtained from (1, 61) included 1,00000 records and 15 features according to Table 1. 5.
section_name: Dataset

section_name: Assessable Criteria

section_content: The main purpose of this study is to increase accuracy. To better evaluate the proposed method, different situations have been considered based on these three scenarios:

• First Scenario: 80 percent of data for learning and the remaining 20 percent for testing • Second Scenario: 60 percent of data for learning and the remaining 40 percent for testing • Third Scenario: 70 percent of data for learning and the remaining 30 percent for testing The first evaluated criterion was accuracy. As is observed, a 30-minute delay had a higher accuracy percentage, a reason for which is that the delay has The second criteria evaluated in Table According to Table (7), the cause of 40% of the delays has been recorded in the airports, the most important of which was air time, followed by delayed arrival. Each of the delay factors alone could record several arrival delays at the subsequent airports, except for the arrival delay factor. Therefore, a significant part of the delay factors was related to delayed arrivals, which will be resolved when airlines have the required time for retrieving and returning to the flight schedule. At present, the cause of delays of less than 15 minutes and departure delays is not recorded at most airports. However, the recording information for the delays between 15 and 30 minutes is more thorough, which leads to higher accuracy and precision.
section_name: Accuracy

section_content: Improving the accuracy and precision of the ATM is a basic method in ATM research. Several ATM approaches have been provided on an ATC level. 

The accuracy of the proposed approach to traffic was low and did not respond to heavy traffic. 

In the present study, an LST-ELM hybrid model was applied to improve the accuracy of the proposed method. 

The comparison of the proposed approach for the 30-minute delay and

(1) and (61) studies is shown in Together, these two algorithms improve accuracy more effectively compared to other methods.
section_name: Comparison of the Proposed Method with Conducted Research

section_content: The improved accuracy in ATM In further studies, to increase the ATM accuracy, other LSTM models like Unlike standard feed-forward neural networks, LSTM has feedback connections. Terminal Control Area(TCA or TMA): A terminal control area (TMA, or TCA in the U. S. and Canada), also known as a terminal manoeuvring area (TMA) in Europe, is an aviation term to describe a designated area of controlled airspace surrounding a major airport where there is a high volume of traffic.
section_name: Conclusion

section_content: Figure 1 ATM Problem-solving Methods

Flowchart of Proposed Method
section_name: Figures

section_name: Acknowledgements

section_content: Acknowledgements The authors are thankful to anonymous reviewers for their valuable comments and suggestions that helped improving the quality of the paper.
section_name: Acknowledgements

section_name: Funding

section_content: Not applicable
section_name: Funding

section_name: Availability of data and materials

section_content: Datasets that have been used for experiments in this paper are available at: kaggle. com
section_name: Availability of data and materials

section_name: Competing interests

section_content: Casc-LSTM and Ens2-LSTM can be used alongside the ELM algorithm. 

One-way and two-way Lstm council can also be used along with other algorithms. 

Ethics approval and consent to participate Not applicable. 

Consent for publication Not applicable.

section_content: The authors have no conflict of interest.
section_name: Competing interests

section_name: Authors' contributions

section_name: Not applicable

section_name: Authors' information