Optimization of air traffic management efficiency based on deep learning enriched by the long short-term memory (LSTM) and extreme learning machine (ELM)

Air traffic management (ATM) refers to the required activities for the efficient and safe management of the national air system (NAS) in each country. Generally, ATM encompasses the two components of air traffic control (ATC) and air traffic follow management [1]. The ATC system mainly utilizes tactical decisions (e.g., real-time separation method) for collision detection. The NAS is divided into several sections to present ATC services and assist air traffic controller operators in the process of traffic control and flight separation by ATCs. The air traffic control methods for the prevention of flight delay and interference is a significant issue in the operating field of ATM [1]. Additionally, fleet fuel and flight delays in airports and secondary costs impose significant multiple charges to the airlines.

The arrival schedule of flights is considered by airlines and aviation/airport companies [2], attracting attention to the airports with better ATM performance by airlines. Another challenge of flight control is several airports' coverage by a single ATM while each airport may have several pattern areas for its ATM [3]. 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 [6‐17]. Some scholars have attempted to address the issue of traffic control and delay only by considering climatic and environmental conditions [10, 18‐20]. Other studies have used the first come first serve (FCFS) technique, along with the queue model and other mixed methods, to solve the problem [21‐25].

Another category of research and articles have applied data mining methods to investigate the influential factors in air traffic and flights [19, 26‐30]. The machine learning algorithm is a conventional method which used to resolve the issues of air traffic, ASL, delay forecast, and minimization.

Machine learning is a branch of artificial intelligence or data analytics that deals with the development of algorithms that can be configured to learn from previous information. Machine learning is indeed a computational method for data mining [31, 32].

Overall, this technique has shown better problem-solving efficiency compared to other methods [33‐37]. Some studies have also supported the uncertainty and fuzzy states, incorporating the latter into other techniques [38‐40]. Such solutions have been proposed for ATC problems and are based on the Internet of Things (IoT) [16, 41, 42], optimization methods [6, 10, 12, 43], multi-objective optimization techniques [44‐46], and intelligent agent-based methods [47].

An optimal approach to solving the mentioned issue involves using the structure of artificial neural networks [25, 43, 48]. Following the evolution of neural networks, deep learning networks have been considered to be one of the most recent and complete solutions in this regard. This novel technique could solve problems with a high accuracy owing to its ability to accept the large data of the problem and neural network integration, as well as learning techniques and structural dynamism, in the hidden layers formation. Aviation and ATM issues are no exception, and most of the recent studies regarding flight delay forecast and flight control traffic have benefited from this technique [1, 47, 49‐51].

Multilayer neural networks or deep neural networks are included in machine learning subject and a set of algorithms that attempt to model high-level abstract concepts based on learning at various levels and layers, thereby enabling deep learning to process large volumes of data in complicated categories [1, 49, 50, 52‐54]. In the current research, it was attempted to propose an accurate and proper method to solve the problem within the operational domain of the terminal management area (TMA) using a combination of a deep neural network and other methods. Furthermore, the present study is aimed to propose a deep learning model using a long short-term memory (LSTM)-based deep learning model and recurrent neural network (RNN) in order to increase the predictive accuracy of short and long-term annual windows by enhancing deep learning (two-dimensional). In the third phase, the output of the deep model was transferred to the extreme learning machine (ELM) and fast learning deep neural machine in order to calculate the estimated time of arrival (ETA) and estimated time of departure (ETD) of each flight based on other similar input data, including the NAS data, bureau of transportation statistics (BTS) system, and automatic dependent surveillance-broadcast (ADS-B) system. Ultimately, an flights control was developed within the airport TMA range with a 15-min time window for flight arrival using evolutionary and meta-heuristic algorithms by conforming the flight rules to the learning outcomes and increasing the accuracy.

The following sections of the article have been structured, with section two reviews the previous methods in the ATM field, section three describes the proposed model, and section four, evaluates and compares the results with other techniques; in addition, section five has been dedicated to a conclusion.

Numerous efforts have been made to solve the ATM problem and minimize the rates of ETA and ETD delay in various dimensions. Most of the studies in this regard have evaluated inbound and outbound flights separately, attempting to propose solutions using methods such as data mining and mathematical techniques (Fig. 1).

In [1], researchers used deep learning architectures such as stacked auto encoders, convolutional neural networks, and recursive neural networks as the architecture for forecasting daily delay status. The aim of the present study was to estimate the daily delay at each airport and calculate the delay for a specific flight based on the obtained results. In order to forecast the daily delay, we initially calculated the mean delay of the inbound and outbound flights and added the estimated value to a recursive neural network, along with the weather data as a sequence, which was added to the output after determining the weight and bias of the separate data. Weighting and biasing are repetitive procedures, and each replication stage determines the value of cost function using the stacked memory cell structure (LSTM) and Sigmoid and Tanh functions were replaced by the structure of the RNN. As a result, the information of each hidden layer was stored, thereby increasing the model efficiency. However, the proposed method limitation was the elimination of the details from the rounds related to the management of the ground delays for the flight preparation of the aircrafts. Another issue was the lack of a deeper LSTM structure in a time-based forecast structure, which increased accuracy.

In [26], the main objective was to minimize the estimated delay along with the sequence of the flights in resolving the ATM problem; a combination of clustering and neural network was used to solve the problem. The integrated technique had two steps of clustering-based forecast and multi-cell neural network (MCNN)-based forecast. In the first step, principal component analysis (PCA) was used to reduce the variable dimensions of the path vector after the clearing, filtering, and re-analyzing of data. Afterwards, the paths were clustered into several patterns with a clustering algorithm. In the forecast phase, the MCNN model was applied to predict the four-dimensional (4D) aspect of the density path. In addition, there was a predictor for each path partition, which encompassed an NN-based learning cell. Each exclusive learning cell was trained by a set of related paths, and each paths set included the related prediction model. According to the obtained results, the proposed model in present study was stronger, more accurate, and more efficient for short-term predictions. However, some of its limitations were the low-data scale and lack of highly accurate learning methods (e.g., deep machine learning). These challenges were eliminated by using a large data volume and the deep learning technique.

Researchers have used a combination of deep belief networks (DBNs) and PCA to evaluate the aviation safety of the country. In general, DBN is useful for safety prediction since each layer acquires more complicated features from the previous layers. In fact, DBN predicts severe flight incident rates based on PCA results [49]. In this case, we assessed seven main factors for unsafe events systematically and in detail, including aircraft, landing and takeoff, aircraft operation, airport and aircraft, ground transportation, and weather. According to the obtained results, the predicted PCA-DBN data were compatible with the actual data on flight incidents. In this regard, the proposed model was considered superior compared to the gray neural network, support vector machine, and DBN. In [41], the researchers proposed a deep learning-based model to predict the hard landings of aircrafts based on the quick access record (QAR).

In IoT environment, devices collect data, which are sent to the IoT open cloud platform to be processed and analyzed. The prediction of a hard landing is a common application of IoT in the aviation field. Initially, 15 aircraft landing sensor data were selected from 260 parameters based on meteorology. Afterwards, an LSTM-based deep prediction model was developed to predict hard landing incidents using the selected sensor data. The empirical results showed high performance owing to the increased accuracy of the QAR data. The proposed model in this research was accurate and efficient in the prediction of hard landings, which guarantees the passengers safety and the decreased rate of flight incidents.

In [39], the main goal was to detect traffic accidents in order to increase road safety using the deep learning algorithm and stacked auto encoder model. In addition, the back propagation algorithm was used for the accurate adjustment of the parameters in the deep network. Ultimately, a fuzzy controller was exploited to increase the output accuracy of the deep network and adjust the neural network learning parameters based on the mean squared error (MSE). According to the findings, the fuzzy logic systems could be suitable for uncertain or approximate reasoning and allowed decision-making with the estimated values in incomplete or uncertain information.

In [28], the researchers presented various methods of data mining in the air transport lounge and assessed their efficacy. The proposed methods were assessed in three types of air transport data, and the flight recording information was provided by a flight recorder for the first time. The unofficial flight information recorder is known as the black box. In an aircraft equipped with a flight recorder, usually up to 500 variables of information are recorded per second for the flight duration, such as time, altitude, vertical acceleration, and vertex. While some of these variables are distinct, the others may be continuous. The artificial data were the second type of the aviation information. This information were focused on flight anomalies. This concept intentionally embedded in the data to examine the ability of the algorithm to detect the anomalies.

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

As for labels and labeled data in aviation data mining, a label is a descriptive word allocated to data based on specific features. In the present study, labels were considered a factor for the formation of a flight incident.

Some of the factors cause to flight incidents included diseases, hazardous environments and autopilot. To improve the accuracy of flight characteristics, the researchers used the time warp edit distance (TWED) and k-means algorithms [29]. In first, the researchers assessed a dataset of flights with the desired time in the case of flight routing with the same origin and flight destination to eliminate the effect of the exit point. Then, the adapted k-means algorithm was proposed, in which the distance between various paths was estimated by the TWED algorithm rather than the conventional elastic similarity measurement in the k-means algorithm. In this research, one of the benefits of the proposed method was the increased accuracy of the algorithm and higher efficacy of using the controlled airspace in air traffic management. On the other hand, one of the key limitations of the method was not considering the large scale of the data and use of large data, which led to the higher accuracy of the algorithm.

In [55], the researchers proposed a hybrid method of Bayes method and Gaussian mixture model–expectation maximization algorithm (GMM)–EM algorithm to predict and analyze the influential factors in the delay of the flights in Brazil aviation routes. Initially, the degree of the impact of each factor was calculated using outdated data. Then Bayes rules at specific points of the flight route followed by determining whether the delays occurred in larger domains. The next stage involved the estimation of the probability of the delays using the GMM–EM and EM algorithms, which are based on similarity in data. According to the obtained results, the probability of the delays at high levels could be predicted by determining the factors at low levels. Moreover, the GMM–EM algorithm could find more values for the similarity function compared to the EM algorithm, thereby reaching convergence sooner. Moreover, the accuracy of the model was observed to increase, which in turn improved the reliability of the prediction results.

In [8], the researchers focused on real-time aircraft routing and planning. In a crowded traffic control area (TCA), problems occur in case of traffic, which is specifically challenging for TCA operation management due to the growing demand for traffic, and the TCAs turn into the bottleneck of the entire ATC system. In this research, the method of linear programming formulations along with flight safety rules has been used to solve the problem, in order to minimize the maximum delay in the entire travel time due to the potential of aircraft congestion. Computational tests have been performed on real data from Rome Airport, the largest airport in Italy in terms of passenger demand.

The solution provides the optimal compromise among various objectives. In [9], the researchers proposed a new, efficient computational algorithm to resolve the uncertainty of the air traffic follow management using a limited probability optimization method. They initially developed a chance-constrained model based on the previous integral planning optimization model of the ATFM for the limitation of the possible capacities of the section. Afterwards, a polynomial approximation-based approach was applied to manage the chance optimization problems at large scales. One of the benefits of the proposed method was considering the uncertainty states in ATM, while the main limitation of the technique was the lack of using deep learning methods for large data in order to obtain a more accurate model.

In [23], the researchers estimated the input delay time and number of the aircrafts entering a controller space at a single time using a queue model and regression function, while also considering climatic conditions. In addition, the delay was forecasted before reaching the destination by considering variables such as the type of the aircraft, time of arrival, and times of entering and exiting the control space. The overall results of the optimization and artificial intelligence-based operation methods demonstrated that the artificial intelligence methods could overlook some of the errors, which rendered them extremely more accurate compared to the queue models. 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 [56]. Changes in spatial–temporal air movements could be comprehensively considered by using this algorithm. In the mentioned study, the traffic situation graphics (TSG) sequence was applied to extract the prominent features. The proposed TSG enabled the consideration of some real-time factors to enrich the input information. As such, the model input was determined by combining the traffic situations at various light levels with the areas that were specified by other real-time factors, such as important tasks and public air traffic lights. The length of the input sequence was set to 30, 60, and 90 min before the prediction moment in order to determine the effect of the temporal dependencies, so that the optimal architecture proposed could be selected. Furthermore, the evaluation of the prediction results along with the three statistical factors confirmed the ability of the proposed model to yield accurate and sustainable predictive results for the air traffic system by distribution at various optical levels.

In [57], the main objective was to predict flight routes using a deep neural network in a capacity management and air traffic operational system. A deep neural network was trained in the historical routes and a set of predictors, and the neural network predicted the most likely route through the airspace. In addition, the network was able to generalize the results to the flights and conditions that had not been detected before. The neural network could also prevent changes by repetitive educations on the newly recorded data. In the mentioned study, an integrated solution was used in the air traffic platforms with the capacity for 10% of the total traffic, and the results of the solution showed the level of the apparent progress.

The promotion of user confidence increases the domain of all traffic.

Large European airports consider strategic flight plans to reduce the air traffic capacity that demand imbalance. In these airports, flights are assigned an entry or departure slot a few months before takeoff. In this regard, the researchers in [58] evaluated such strategic plans using the predictions of the flight delays arriving, departing or canceling. The proposed approach was used in London Heathrow Airport during 2013–2018, and the resulting flight plan was assessed in terms of the predicted flight cancelation and delay using a machine learning approach. According to the findings, the proposed method was able to provide the airport coordinators of the possible delays and cancelations related to the strategic plans. In [59], an end-to-end deep learning-based approach was also presented to increase accuracy in the air traffic flow using the CNN and RNN algorithms, as well as a convolutional LSTM module, which was proposed to construct a trainable model to predict the air traffic flow. The experimental results of the actual data were indicative of the superior performance compared to the current approaches used for predictive accuracy and stability.

Moreover, the proposed model could predict the flow distribution at various flight levels in the flights controlled space, which in turn improved the ATM level. The analysis of the distribution of the prediction errors on various spaces cells, flight levels, and prediction of the samples indicated that the spatial and temporal transmission patterns of the flight flow in the ATM system could be thoroughly learned by the proposed model. On the other hand, the proposed model could predict that the optimal air traffic management measures were taken to improve performance efficacy system.

In [17], the researchers used bidirectional long short-term memory (BLSTM) for the performance data of air transportation management in order to identify the system. In the system, BLSTM was able to reconstruct the nonlinear temporal series and make valid predictions. According to the other findings of the mentioned study, neural networks in deep learning methods could manage complicated nonlinear temporal series and learn to reconstruct these series based on multidimensional inputs, while also storing their knowledge regarding the behavior of the observation datasets.

In [60], a multi-step deep sequence learning model (Bi-LSTM + Seq2Seq) was proposed to predict airport delay based on the spatial and temporal relations of the other airports within the network. In the first step, the dataset was processed for the analysis the correlation between the temporal delays of the airports based on the complex network theory. Afterwards, the PageRank and K-means algorithms were applied to cluster the behavior of the networks and identify their overall status. At the next stage, the Bi-LSTM + Seq2Seq model was proposed and trained based on the time-series data on the current status of the network and delay in the interactions between the airports. The experimental results indicated that the suggested model had higher accuracy and sustainability compared to other prediction algorithms.

In [31], the main objective was proposing a deep learning-based method to evaluate the delays in inbound flights. Initially, the important features were extracted, followed by model training by artificial neural networks and DBN using random samples. In the mentioned study, researchers applied the momentum learning rate and resilient back propagation, which acted extremely quicker than back propagation, thereby increasing the training pace and model convergence. Notably, the DBNs were based on a Boltzmann machine, where each layer received communications from the previous layer, and a Boltzmann machine was added to the network at each stage. During the training, the inaccurate classification error rate decreased by the fine-tuning of the parameters and momentum learning rate. Since the output of each layer was divergent, the training pace decreased, and the gradient tended to zero.

The proposed model in the present study was based on the LSTM and ELM algorithms. Figure 2 depicts the flowchart of the proposed method. As is observed in Fig. 2, the suggested method had three phases of the loading, normalization, and separation of the data, creating a two-dimensional LSTM back learning structure using a Bi-LSTM neural network, while also estimating the beta weights, training the ELM, and calculating the assessed criteria. The proposed steps have been further explained.

At this stage of the research, we are analyzed and evaluated the applied data and assessed the results and criteria.

The improved accuracy in ATM management problems is proposed in this paper. ATM includes all the necessary activates for the safe and useful management of the National Aviation System which is one the most challenging problems in our country's airports right now. In this paper, the dual-sided LSTM algorithm is used to improve the 15 and 30-min delays’ accuracy. Also, this algorithm is used to improve the ELM algorithm's parameters. The data set used in this paper is taken from Kaggle and is a simulation used by MATLAB. The results show a higher accuracy improve the rate in comparison to other paper and also show that the RMSE parameter in 30-min delays has a lower percentage in ORD, PHX, and JFK airports in comparison to the other airports.

In further studies, to increase the ATM accuracy, other LSTM models like 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.