A multi-level predictive methodology for terminal area air traffic flow

Over the past few decades, the air transportation system has grown significantly. In particular, the number of passengers using air transportation has greatly increased. As the demand for air travel expands, airport departure/arrival demand almost reaches its capacity. In consequence, the level of delays increases since the system capacity cannot manage the increased demand. With this trend, the national airspace system (NAS) will be saturated, and the congestion at the airport will become even more severe. As a result of congestion, a considerable number of flights experience delays. According to the Bureau of Transportation Statistics (BTS), over 1 million flights are operated in a year, and about twenty percent of all scheduled commercial flights are delayed more than 15 minutes. These delays cost billions of dollars annually for airlines, passengers, and the US economy. Therefore, this study seeks to find out why the delays occur and to analyze patterns in which the delays occurred. Analysis of airport operations generally falls into a macro or micro perspective. At the macro point of view, very few details are considered, and delays are aggregated at the airport level. Especially, shortfalls in airport capacity and a capacity-demand imbalance are the primary causes of delays in this respect. In the micro perspective, each aircraft is modeled individually, and the causes of delays are reproduced as precisely as possible. Micro reasons for air traffic delays include inclement weather, mechanics problems, operation issues. In this regard, this research proposes a methodology that can efficiently and practically predict macro and micro-level air traffic flow in the terminal area. For a macro-level analysis of delays, artificial neural networks models are proposed to predict the hourly airport capacity. Multi-layer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM) are trained with historical weather and airport capacity data of Hartsfield-Jackson Atlanta airport (ATL). In the performance evaluation, the models have presented decent predictive performance and successfully predicted the test data as well as the training data. On the other hand, Random Forests and AdaBoost are implemented in the micro-level modeling of the air traffic. The micro-level models trained with on-time flight performance data and corresponding weather data focus on a classification of the individual flight delays. The model provides interpretability and imbalanced data handling while the accuracy is as good as the existing methods. Lastly, the predictive model for individual flight delays is refined using the cost-proportionate rejection sampling (costing) method. Along with the integration of the costing method, general machine learning algorithms have been converted to cost-sensitive classifiers. The cost-sensitive classifiers were able to account for asymmetric misclassification costs without losing their diagnostic functionality as binary classifiers. This study presents a data-driven approach to air traffic flow management that can effectively utilize air traffic data accumulated over decades. Through data analysis from the macro and micro perspective, an integrated methodology for terminal air traffic flow prediction is provided. An accurate prediction of the airport capacity and individual flight delays will assist stakeholders in taking more informed decisions.Ph.D

Encoder-Decoder Approach to Predict Airport Operational Runway Configuration A case study for Amsterdam Schiphol airport

The runway configuration of an airport is the com- bination of runways that are active for arrivals and departures at any time. The runway configuration has a major influence on the capacity of the airport, taxiing times, the occupation of parking stands and taxiways, as well as on the management of traffic in the airspace surrounding the airport. The runway configuration of a given airport may change several times during the day, depending on the weather, air traffic demand and noise abatement rules, among other factors. This paper proposes an encoder-decoder model that is able to predict the future runway configuration sequence of an airport several hours upfront. In contrast to typical rule-based approaches, the proposed model is generic enough to be applied to any airport, since it only requires the past runway configuration history and the forecast traffic demand and weather in the prediction horizon. The performance of the model is assessed for the Amsterdam Schiphol Airport using three years of traffic, weather and runway use data.Peer ReviewedPostprint (published version

Deep Learning Prediction Models for Runway Configuration Selection and Taxi Times Based on Surface Weather

Growth in air traffic demand in the United States has led to an increase in ground delays at major airports in the nation. Ground delays, including taxi time delays, directly impacts the block time and block fuel for flights which affects the airlines operationally and financially. Additionally, runway configuration selection at an airport significantly impacts the airport capacity, throughput, and delays as it is vital in directing the flow of air traffic in and out of an airport. Runway configuration selection is based on interrelated factors, including weather variables such as wind and visibility, airport facilities such as instrument approach procedures for runways, noise abatement procedures, arrival and departure demand, and coordination of ATC with neighboring airport facilities. The research problem of this study investigated whether runway configuration selection and taxi out times at airports can be predicted with hourly surface weather observations. This study utilized two sequence-to-sequence Deep Learning architectures, LSTM encoderdecoder and Transformer, to predict taxi out times and runway configuration selection for airports in MCO and JFK. An input sequence of 12 hours was used, which included surface weather data and hourly departures and arrivals. The output sequence was set to 6 hours, consisting of taxi out times for the regression models and runway configuration selection for the classification models. For the taxi out times models, the LSTM encoder-decoder model performed better than the Transformer model with the best MSE for output Sequence 2 of 41.26 for MCO and 45.82 for JFK. The SHAP analysis demonstrated that the Departure and Arrival variables had the most significant contribution to the predictions of the model. For the runway configuration prediction tasks, the LSTM encoder-decoder model performed better than the Transformer model for the binary classification task at MCO. The LSTM encoder-decoder and Transformer models demonstrated comparable performance for the multiclass classification task at JFK. Out of the six output sequences, Sequence 3 demonstrated the best performance with an accuracy of 80.24 and precision of 0.70 for MCO and an accuracy of 77.26 and precision of 0.76 for JFK. The SHAP analysis demonstrated that the Departure, Dew Point, and Wind Direction variables had the most significant contribution to the predictions of the model

Improving the predictability of take-off times with Machine Learning : a case study for the Maastricht upper area control centre area of responsibility

The uncertainty of the take-off time is a major contribution to the loss of trajectory predictability. At present, the Estimated Take-Off Time (ETOT) for each individual flight is extracted from the Enhanced Traffic Flow Management System (ETFMS) messages, which are sent each time there is an event triggering a recalculation of the flight data by the Network Man- ager Operations Centre. However, aircraft do not always take- off at the ETOTs reported by the ETFMS due to several factors, including congestion and bad weather conditions at the departure airport, reactionary delays and air traffic flow management slot improvements. This paper presents two machine learning models that take into account several of these factors to improve the take- off time prediction of individual flights one hour before their estimated off-block time. Predictions performed by the model trained on three years of historical flight and weather data show a reduction on the take-off time prediction error of about 30% as compared to the ETOTs reported by the ETFMS.Peer ReviewedPostprint (published version

A Deep Learning Approach for Real-time Crash Risk Prediction at Urban Arterials

Real-time crash risk prediction aims to predict the crash probabilities within a short time period, it is expected to play a crucial role in the advanced traffic management system. However, most of the existing studies only focused on freeways rather than urban arterials because of the complicated traffic environment of the arterials. This thesis proposes a long short-term memory convolutional neural network (LSTM-CNN) to predict the real-time crash risk at arterials. The advantage of this model is it can benefit from both LSTM and CNN. Specifically, LSTM captures the long-term dependency of the data while CNN extracts the time-invariant features. Four urban arterials in Orlando, FL are selected to conduct a case study. Different types of data are utilized to predict the crash risk, including traffic data, signal timing data, and weather data. Various data preparation techniques are applied also. In addition, the synthetic minority over-sampling technique (SMOTE) is used for oversampling the crash cases to address the data imbalance issue. The LSTM-CNN is fine-tuned on the training data and validated on the test data via different metrics. In the end, five other benchmarks models are also developed for model comparison, including Bayesian Logistics Regression, XGBoost, LSTM, CNN, and Sequential LSTM-CNN. Experimental results suggest that the proposed LSTM-CNN outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this thesis indicate the promising performance of using LSTM-CNN to predict real-time crash risk at arterials

Kratkoročna prognoza vidljivosti određena metodom slučajne šume

Accurate visibility forecasting is essential for safe aircraft operations. This study examines how various configurations of the Random Forest model can enhance visibility predictions. Preprocessing techniques are employed, including correlation analysis to identify fundamental relationships in weather observations. Time-series data is transformed into a regular Data Frame to facilitate analysis. This study proposes a classification framework for organizing visibility data and phenomena, which is then used to develop a visibility forecast using the Random Forest method. The study also presents procedures for hyperparameter tuning, feature selection, data balancing, and accuracy evaluation for this dataset. The main outcomes are the Random Forest model parameters for a three-hour visibility forecast, along with an analysis of errors in low visibility forecasts. Additionally, models for one-hour forecasts and visibility forecasting under precipitation are also examined. The resulting models demonstrate a deterministic forecast accuracy of approximately 78%, with a false alarm rate of around 6%, providing a comprehensive overview of the capabilities of the Random Forest model for visibility forecasting. As anticipated, the model demonstrated limitations in accurately simulating fast radiative cooling or abrupt decreases in visibility caused by precipitation. Specifically, in relation to precipitation, the model achieved an accuracy of 79%, yet exhibited a false alarm rate of 19%. Additionally, this method sets a foundation for enhancing prediction accuracy through the inclusion of supplementary forecast data, while its implementation on real-world datasets expands the reach of machine learning techniques to the members of the meteorological community.Točno predviđanje vidljivosti ključno je za sigurne operacije zrakoplova. Ova studija ispituje kako različite konfiguracije modela slučajne šume (eng. Random Forest) mogu poboljšati predviđanja vidljivosti. Koriste se tehnike predprocesiranja, uključujući analizu korelacije za prepoznavanje temeljnih odnosa u promatranjima vremena. Podaci vremenskih nizova pretvaraju se u redoviti podatkovni okvir kako bi se olakšala analiza. Ova studija predlaže klasifikacijski okvir za organiziranje podataka o vidljivosti i meteoroloških pojava. Taj okvir se zatim koristi za razvoj prognoze vidljivosti korištenjem metode slučajne šume. Studija također prikazuje postupke za podešavanje hiperparametara, odabir značajki, uravnotežavanje podataka i procjenu točnosti za taj skup podataka. Glavni rezultati su parametri modela slučajne šume za trosatnu prognozu vidljivosti te analiza pogrešaka prognoze slabe vidljivosti. Dodatno, ispitani su i modeli za jednosatnu prognozu i prognozu vidljivosti u slučaju oborine. Dobiveni modeli pokazuju točnost determinističke prognoze od približno 78%, uz oko 6% lažnih uzbuna, dajući sveobuhvatan pregled mogućnosti modela slučajne šume za predviđanje vidljivosti. Kao što se i očekivalo, model je pokazao ograničenja pri simulaciji brzog radijacijskog hlađenja i pri naglom smanjenju vidljivosti uzrokovanom oborinama. Naime, u odnosu na oborine, točnost modela je bila 79%, ali stopa lažnih uzbuna iznosila 19%. Dodatno, metoda slučajne šume postavlja temelje za poboljšanje točnosti prognoza uključivanjem dodatnih prognostičkih podataka, dok njezina primjena na skupove realnih podataka proširuje primjenu tehnika strojnog učenja na na meteorološke probleme

Forecasting model development and application in the aviation industry

Forecasting models have been applied to many industries as a decision-making tool for over 100 years. Their application in the aviation industry benefits a wide variety of stakeholders such as airliners and airport authorities, who use past data to predict demand and passenger choices so that they can better define fares, manage their fleet and make decisions on the airport layout and future expansions, among others. The main objective of this dissertation is the development of a forecasting model capable of predicting the number of flight movements at Lisbon Airport. The model was based on an autoregressive model, which uses past data in order to forecast future figures. Weekly data regarding the flight movements at Lisbon Airport was the sample for this study, which was processed through RStudio programming software. Once the Autoregressive Moving Average (ARIMA) models were defined, the forecasting data was created and further tested for accuracy using extant data. The impact of COVID-19 had to be considered in this situation, leading to the breakdown of the original time-series into three different samples. The forecasting models were subsequently created through each of these models. The results were expressed through the three different models, and since two of them have extant data, meaning an existing sample to compare the predicted data, it was possible to determine the accuracy level. However, these models cannot be applied immediately since the impact of COVID-19 is still present and flights have not resumed normality. Once this normality resumes, the models can be applied.Modelos preditivos têm sido aplicados a variados setores como ferramenta de tomada de decisão há mais de 100 anos. A sua aplicação na indústria aeronáutica beneficia uma ampla variedade de interessados, como companhias aéreas e autoridades aeroportuárias que utilizam dados para prever a procura, definir preços, gerir frotas e tomar decisões relativas ao layout do aeroporto, expansões futuras, entre outros. O principal objetivo desta dissertação é o desenvolvimento de um modelo de previsão capaz de prever o número de movimentos de voos no Aeroporto de Lisboa. O modelo foi baseado num modelo autorregressivo, que utiliza dados passados para prever valores futuros. O Aeroporto de Lisboa foi o objeto escolhido para esta dissertação. Dados semanais relativos aos movimentos aéreos no Aeroporto de Lisboa consistiram na amostra para este estudo, os quais foram processados através do software de programação RStudio. Assim que os modelos Autoregressive Moving Average (ARIMA) foram definidos, os dados de previsão foram criados e testados quanto à precisão usando os dados existentes. O impacto do COVID-19 teve que ser considerado nesta situação, levando à divisão da série temporal original em três amostras diferentes. Os modelos de previsão foram posteriormente criados através de cada um desses modelos. Os resultados foram expressos através dos três modelos, e como dois deles possuem dados existentes para comparação com dados previstos, foi possível determinar o nível de precisão. No entanto, os modelos não podem ser aplicados imediatamente, uma vez que o impacto do COVID-19 ainda está presente e os voos não voltaram à normalidade. Uma vez resumida essa normalidade, os modelos podem ser aplicados

Prediction of flight delay using deep operator network with gradient-mayfly optimisation algorithm

Accurate flight delay prediction is fundamental to establishing an efficient airline business. It is considered one of the most critical intelligent aviation systems components. Recently, flight delay has been a significant cause that deprives airlines of good performance. Hence, airlines must accurately forecast flight delays and comprehend their sources to have excellent passenger experiences, increase income and minimise unwanted revenue loss. In this paper, we developed a novel approach that is an optimisation-driven deep learning model for predicting flight delays by extending a state-of-the-art method, DeepONet. We utilise the Box-Cox transformation for data conversion with a minimal error rate. Also, we employed a deep residual network for the feature fusion before training our model. Furthermore, this research uses flight on-time data for flight delay prediction. To validate our proposed model, we conducted a numerical study using the US Bureau of Transportation of Statistics. Also, we predict the flight delay by selecting the optimum weights using the novel DeepONet with the Gradient Mayfly Optimisation Algorithm (GMOA). Our experiment results show that the proposed GMOA-based DeepONet outperformed the existing methods with a Root Mean Square Error of 0.0765, Mean Square Error of 0.0058, Mean Absolute Error of 0.0049 and Mean Absolute Percent Error of 0.0043, respectively. When we apply 4-fold cross-validation, the proposed GMOA-based DeepONet outperformed the existing methods with minimal standard error. These results also show the importance of optimisation algorithms in deciding the optimal weight to improve the model performance. The efficacy of our proposed approach in predicting flight delays with minimal errors well define from all the evaluation metrics. Also, utilising the prediction outcome of our robust model to release information about the delayed flight in advance from the aviation decision systems can effectively alleviate the passengers’ nervousness.UKRI for the COVID-19 recovery grant under the budget code SA077N. This research was heavily affected by the COVID-19 pandemic during the first authors' PhD studies. This lead to an extension to registration for 3 months, which was funded by the UKRI doctoral extension recovery grant. (PTDF main funder of PhD)