Flight Delay Prediction Using a Hybrid Deep Learning Method

The operational effectiveness of airports and airlines greatly relies on punctuality. Many conventional machine learning and deep learning algorithms are applied in the analysis of air traffic data. However, the hybrid deep learning (HDL) model demonstrates great success with superior results in many complex problems, e.g. image classification and behaviour detection based on video data. Interestingly, no previous attempts have been made to apply the concept of HDL in analysing structured air traffic data before. Hence, this research investigates the effectiveness of the HDL in the departure delays severity prediction (i.e. on-time, delay and extremely delay) for 10 major airports in the U.S. that experience high ground and air congestion. The proposed HDL model is a combination of a feed-forward artificial neural network model with three hidden layers and a conventional gradient boosted tree model (XGBoost). Utilising the passenger flight on-time performance data from the U.S. Department of Transportation, the proposed HDL model achieves a sharp rise of 22.95% in accuracy when compared to a pure neural network model. However, with current data used in this research, a pure machine learning model achieves the best prediction accuracy

Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction

The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment

Delays prediction using data mining techniques for supply chain risk management company

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceGlobalization makes competition in supply chain management more intense. Pressure on improving the efficiency, guarantee that goods arrive on time and reduce the cost of shipment became higher. Shipment passes through different continents and cultures, dispersed around the world and encounter different conditions and risks. These risks are unexpected events that might disrupt the flow of materials or the planned operations. It can be due to late delivery, inaccuracy in forecasting, natural disasters like hurricane and earthquake or sociocultural events like strike. An effective use of supply chain risk management methods which includes risk identification, risk assessment, risk mitigation, and risk control is important for the organization to survive. For that reason, I was part of a team in XXX organization who has a goal to develop a predictive model to predict shipment delays for company’s customers

A deep BiLSTM machine learning method for flight delay prediction classification

This paper proposes a classification approach for flight delays using Bidirectional Long Short-Term Memory (BiLSTM) and Long Short-Term Memory (LSTM) models. Flight delays are a major issue in the airline industry, causing inconvenience to passengers and financial losses to airlines. The BiLSTM and LSTM models, powerful deep learning techniques, have shown promising results in a classification task. In this study, we collected a dataset from the United States (US) Bureau of Transportation Statistics (BTS) of flight on-time performance information and used it to train and test the BiLSTM and LSTM models. We set three criteria for selecting highly important features to train and test the models. The performance evaluation of the models and Confusion matrix shows that BiLSTM outperforms the LSTM model. In evaluating the models using the Mathews Correlation Coefficient (MCC), the BiLSTM model offers a better correlation of 0.99 between the original and predicted classes. Our experiment shows that for predicting flight delays, the BiLSTM model takes advantage of the forward and backward hidden sequences and the deep neural network for performance exploration and exploitation to achieve high accuracy, recall, and F1-Score. Our findings suggest that the BiLSTM model can effectively predict flight delays and provide valuable information for airlines, passengers, and airport managers

Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction

The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment.The Petroleum Trust Development Fund (PTDF) Nigeri

Predictive modelling : flight delays and associated factors hartsfield–Jackson Atlanta international airport

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAtualmente, um ponto negativo nas viagens de avião são os atrasos que, constantemente, são anunciados aos passageiros resultando numa diminuição da sua satisfação enquanto clientes. Este e outros fatores fazem com que elevados custos, tanto quantitativos como qualitativos sejam imputados às companhias. Consequentemente, existe a necessidade de prever e mitigar a existência de atrasos aéreos que pode ajudar as companhias aéreas bem como aeroportos a melhorar a sua performance e a aplicar algumas medidas, dirigidas ao consumidor, que permitiam atenuar ou até anular o efeito que estes atrasos provoca nos seus passageiros. Deste modo, este estudo tem como principal objetivo prever a ocorrência de atrasos nas chegadas ao aeroporto internacional de Hartsfield-Jackson. Esta estimativa será possível através da elaboração de um modelo preditivo, recorrendo a diversas técnicas de Data Mining. Com a aplicação destas técnicas, foi possível identificar as variáveis que mais contribuíram para a existência do atraso. No desenvolvimento deste trabalho, foi seguida a metodologia da descoberta de conhecimento em base de dados (conhecida em inglês por Knowledge Discovery Database, KDD). Fases como a recolha dos dados, a aplicação de técnicas de amostragem (SMOTE e Undersampling), a partição dos dados em treino e teste, o pré-processamento (dados omissos e outliers) e transformação dos dados (normalização dos dados e seleção de atributos), a definição de modelos a treinar (Decision Trees, Random Forest e Multilayer Perceptron) bem como a avaliação da performance dos modelos através de métricas variadas foram aplicadas. Depois de testar diferentes abordagens, concluiu-se que o melhor modelo é alcançado com as variáveis relacionadas com a partida, usando o algoritmo Multilayer Perceptron e aplicando a técnica de SMOTE para lidar com dados não balanceados, removendo outliers e selecionando dez variáveis usando GainRatio. Por outro lado, quando as variáveis com informação da partida são excluídas, o algoritmo que melhor se destaca é o Multilayer Perceptron usando a técnica SMOTE, mas desta vez, incluindo os outliers e com quinze variáveis selecionadas novamente pelo GainRatio. Em ambas as hipóteses, as variáveis explicativas que mais contribuem para a existência do atraso na chegada são relacionadas com o clima, com as características do avião e com a propagação do atraso. Os resultados do algoritmo de Random Forests mostraram melhor desempenho, em relação à precisão, em comparação com outros autores (Belcastro, Marozzo, Talia, & Trunfio, 2016; Choi, Kim, Briceno, & Mavris, 2016). Contrariamente, o algoritmo Multilayer Perceptron, apresentou menor precisão em comparação com outro estudo equivalente (Y. J. Kim, Choi, Briceno, & Mavris, 2016).Nowadays, a downside to traveling is the delays that are constantly advertised to passengers resulting in a decrease in customer satisfaction. These delays associated with other factors can cause costs, both quantitative and qualitative. Consequently, there is a need to anticipate and mitigate the existence of airborne delays that can help airlines and airports improving their performance or even take some consumer-oriented measures that can undo or attenuate the effect that these delays have on their passengers. This study has as primary objective to predict the occurrence of arrival delays of the international airport of Hartsfield-Jackson. It was possible by building a predictive model, applying several Data Mining techniques. With these applications, it was possible to show the variables, among the proposals, that most contributed to the existence of the delay. In this work, the Knowledge Discovery Database (KDD) methodology was followed. Phases such as data collection; sampling techniques (SMOTE and Undersampling); Data partitioning in training and testing; Pre-processing (missing data and outliers) and data transformation (data normalization and attribute selection); And, finally the definition of models to be trained (Decision Trees, Random Forests, and Multilayer Perceptron), as well as the evaluation of the performance of the models through varied metrics, were used. After testing different approaches, it was concluded that the best model is achieved with the variables related to departure, using the Multilayer Perceptron algorithm and applying SMOTE to deal with unbalanced data, removing outliers and selecting ten variables using GainRatio. On the other hand, when the variables with information of the departure are excluded, the algorithm that performs best is also the Multilayer Perceptron using the SMOTE technique but, this time, including the outliers and with fifteen variables selected again by the GainRatio. On both hypotheses, the explanatory variables that most contributed to the existence of the delay in arrivals were related to the weather, the airplane characteristics and the propagation of the delay. Our results for the Random Forests algorithm shown better performance, regarding accuracy, compared to other authors (Belcastro et al., 2016; Choi et al., 2016). Contrary, for the Multilayer Perceptron algorithm, was presented a lower accuracy compared to another equivalent study (Y. J. Kim et al., 2016)

Time series prediction and forecasting using Deep learning Architectures

Nature brings time series data everyday and everywhere, for example, weather data, physiological signals and biomedical signals, financial and business recordings. Predicting the future observations of a collected sequence of historical observations is called time series forecasting. Forecasts are essential, considering the fact that they guide decisions in many areas of scientific, industrial and economic activity such as in meteorology, telecommunication, finance, sales and stock exchange rates. A massive amount of research has already been carried out by researchers over many years for the development of models to improve the time series forecasting accuracy. The major aim of time series modelling is to scrupulously examine the past observation of time series and to develop an appropriate model which elucidate the inherent behaviour and pattern existing in time series. The behaviour and pattern related to various time series may possess different conventions and infact requires specific countermeasures for modelling. Consequently, retaining the neural networks to predict a set of time series of mysterious domain remains particularly challenging. Time series forecasting remains an arduous problem despite the fact that there is substantial improvement in machine learning approaches. This usually happens due to some factors like, different time series may have different flattering behaviour. In real world time series data, the discriminative patterns residing in the time series are often distorted by random noise and affected by high-frequency perturbations. The major aim of this thesis is to contribute to the study and expansion of time series prediction and multistep ahead forecasting method based on deep learning algorithms. Time series forecasting using deep learning models is still in infancy as compared to other research areas for time series forecasting.Variety of time series data has been considered in this research. We explored several deep learning architectures on the sequential data, such as Deep Belief Networks (DBNs), Stacked AutoEncoders (SAEs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Moreover, we also proposed two different new methods based on muli-step ahead forecasting for time series data. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis makes theoretical, methodological and empirical contributions to time series prediction and multi-step ahead forecasting by using Deep Learning Architectures

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data

The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions. Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models. SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value. The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis. The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies