Exploratory Data Analysis on Aviation Dataset

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

Airplane transportation has many advantages compared to other means of transportation, where this means of transportation is able to cover long distances in a short time. Besides having advantages, of course, air transportation often experiences flight delays which can be caused by several things, including delayed flights due to late arrival (Arrival Delay), delays of the airline itself (Carrier Delay), previously late arrivals (Late Aircraft Delay), air traffic congestion (Nas Delay), security issues (Security Delay) and weather conditions (Weather Delay) The tests carried out were 101,315 US flight delay data from 2017 to 2022. By using the Machine Learning method, the results obtained were that the largest flight delays were caused by late arrivals, namely 386,124,672, where the largest flight delays were by airlines. The airline, namely Southwest Airlines, is 61,474,379 of the total airlines, which is 20, and the biggest departure delay is at Chicago O'Hare International airport, which is in the city of Chicago, IL province, which is 20,912,928 of the total airport, which is 596. This research aims to predict future flights by analyzing current flight data, so that future flights can be better by overcoming or avoiding previous flight delay problems.

Due to the large span of flight delay, numerous influencing factors and large number of flights, the collected flight delay data is characterized by large-scale and imbalance, which is considered as one of the largest challenges in balanced flight delay data classification by using conventional classification algorithms. To solve this problem, an adaptive multi-class classification approach of imbalanced flight delay data using synthetic minority over-sampling technique (SMOTE)-based convolutional neural network with sparrow search algorithm, namely SSA-LTCNN is proposed in this paper. Firstly, the SMOTE is used to reduce the imbalance between the classes of the flight delay data. Then, the qualitative analysis method is employed to determine the key parameters of the CNN, which are optimized by using SSA to establish an optimized CNN model. Finally, the optimized CNN model with more effective feature representation ability is employed to extract discriminative features from the balanced flight delay data automatically. The obtained features in the last fully connected layer of the optimized CNN model are used for flight delay classification. The MIT-BIH Arrhythmia Data is selected to verify the effectiveness of the proposed SSA-LTCNN method and the confusion matrix, classification accuracy, precision, recall and F1 score are also used to evaluate the performance of the proposed SSA-LTCNN method, which can get better performance than RF, DT, LR, CNN and QRSCNN. The actual flight delay data is processed to verify the effectiveness of the proposed classification approach, which can achieve higher accuracy than KNN, NB, UCNN, CNN, SSA-LCNN and SSA-TCNN in different time periods. The experimental results show that the SSA-LTCNN can fast and effectively realize the classification of MIT-BIH Arrhythmia Data and flight delay data.

With the development of the civil aviation transportation industry in recent years, the volume of civil aviation transportation has increased rapidly. Increased carrier costs and reduced airport operating efficiency caused by flight delays have become issues that need to be addressed. How to improve the accuracy of predicting flight arrival delay time is of great significance for improving airport transportation efficiency, rationally scheduling flights and improving passenger comfort. In this paper, the Cat-boost model is utilized on the U.S Domestic airline on-time performance data from U.S. Transportation Administration, combined with the characteristics of the model to determine the influencing factors, and to predict the arrival delays of flights within the United States. The accuracy;precision and some other criterion of the model are given to evaluate the performance on the data. A better effect is obtained: the accuracy reach 80.44% in this case. Finally, the specific delay time is predicted, we found that the support vector machine has the best prediction result for the flight delay time, the average prediction error is 9.733 min, which has a certain reference value for flight operation and airport scheduling.

Flight delay is a major issue in the aviation industry. In commercial aviation, if a flight reaches its destination 15 minutes later than the scheduled arrival, it is said to be delayed. Flight delays cause a great deal of bother to travelers. It could make them late to their booked occasions or miss a corresponding flight, accordingly prompting outrage and dissatisfaction. Likewise, travelers may not generally be entitled for a refund when a postponement happens. Carriers report that couple of the numerous reasons prompting most flight delays are carrier glitches, climate conditions, support issues with the airplane and congestion in air traffic. Rapid development in airline industry has led to an increased number of aircrafts in the skies, this has brought about air-gridlock causing flight delays. Flight delays are not only extremely undesirable financially but also have adverse environmental effects. Air-traffic management is becoming increasingly challenging. The aim of our research work is to predict the delay of flights due to various factors using machine learning and deep learning so as to minimize losses and increase customer satisfaction. This Machine Learning model could be integrated with Airlines systems for the use of staff and customers and it could also rank Airlines and flights based on delays.

Technology is evolving in a rapid pace with its numerous discoveries, and nowadays, the rate is more than ever before. Data Analytics has become a knowledge and a tool which significantly contributing relentlessly to majority of the discoveries since this can fetch insights to reduce man-machine interactions. Prediction is an integral part of data analytics that provides meaningful information from historical data to support decisions. Machine learning and deep learning is the core of any predictive analytics where both have their own strengths and weakness. Aviation industry around the world are facing severe problems by the flight delays caused by several factors. In order to achieve its target to provide a hassle-free journey, Aviation Industry is continuously researching to reduce flight delays. This research will focus mainly to predict airlines flight delays by analyzing flight data, especially, for the domestic Airlines those moves around the United States of America. Data science methodology has been implemented in order to fetch the end prediction. In order to transform the high dimension data into a low dimension Principal component analysis is used. Deep learning algorithms, widely popular and state-of-the-art prediction technology, are implemented in the prediction modeling phase. By empirical observation, the research can come to a conclusion that by following the data science methodology better performance could be unlocked to help the aviation industry.

Article history: Received January 14, 2014 Accepted 10 June 2014 Available online June 16 2014 Flight interruption is one of the most important issues in today’s airline industry. Every year, most airlines spend significant amount of money to compensate flight delays. Therefore, it is important to detect important factors influencing on flight delays. This paper presents an empirical investigation to determine important factors on this issue. The study also asks some decision makers to make pairwise comparison and ranks various factors using the art of analytical hierarchy process. The study determines that technical defects and delayed entry were among the most important factors to blame for flight delays. In addition, announcing the postponement, replacement aircraft and path replacement are among the most important decisions facing managers in the aviation industry during the disruption of the flight. © 2014 Growing Science Ltd. All rights reserved.

Diabetes is a long-term health condition that impacts millions of people worldwide, and correct and timely diagnosis is required to manage it effectively. In the present research study, we propose an improved model of diabetes prediction based on the usage of Big Data Analytics (BDA) and Machine Learning (ML) algorithms for enhancing the predictability of diabetes. Our research is based on the ideas developed in the reference paper, where the application of BDA in enabling healthcare decisionmaking was proposed. With the inclusion of the usage of advanced ML algorithms like Decision Trees, Random Forest, Logistic Regression, Naive Bayes, and Artificial Neural Networks (ANN), we enhance predictability for the diagnosis of diabetes. We employed a comprehensive healthcare dataset with various health metrics, including blood pressure, cholesterol, BMI, smoking, and physical activity. The data was heavily preprocessed, ranging from feature engineering to normalization, to get better model performance. All the models were trained and tested on accuracy, precision, recall, and ROC-AUC. Among the models, Random Forest and ANN were better with higher accuracy, detecting diabetic patients at risk effectively. Our model not only increases prediction accuracy but also facilitates scalability with Big Data technology and practices. The ANN model, in particular, proved effective in finding complex patterns within the data, yielding a reliable solution towards real-time diabetes prediction. This effort illustrates the potential of integrating BDA and ML in healthcare for data-driven decision-making and supporting early diagnosis and management of diabetes. The model is deployed as an interactive user-friendly web application using Streamlet, where users can input health indicators and receive predictions from different models. Through its interactive interface, it facilitates healthcare professionals and patients by offering themreal-time information, thereby ultimately improving diabetes management and patient care.

The article summarizes modern approaches of big data analytics implementation in healthcare, including OMIC’s data. The existing challenges for big data in medicine, barriers for implementation, reasons for slow development of practical usage of big data analytics and artificial intelligence in healthcare are discusses as well as perspective for the nearest future.

Flight delay is the root of Delay Propagation, while delay propagated within a Flight Chain. Busy hub-airport plays an important role in a Flight Chain, since the Initial Delay often happens there. To research more possible impacting factors in the Flight Chain, a busy hub-airport is chosen as the object in this research. After analyzing the complex and uncertain relationship between flight delays and all possible impacting factors, Bayesian network (BN) adapt to model and estimate about flight delay in a busy hub-airport. We proposed two learning methods with three different models. Model I is an estimation model for Arrival Delay based on parameter learning of BN with the Expectation-Maximization (EM) algorithm. Model II is estimation model for Arrival Delay based on structure learning of BN with K2 algorithm. The model learned by K2 is proved more suitable for modeling the estimation of flight delay, with a higher estimating correct rate than Model I. Then Model II is estimation model for Departure Delay with the same algorithm of Model II.

Data-Driven Robust Aircraft Assignment to Minimize Delay Propagation

The purpose of this investigation is to examine the role of big data analytics and data availability on sustainable competitive advantage and business performance. Furthermore, the researchers also investigate the mediating role of innovative capabilities between observed variables. The current research used quantitative research design and positivism philosophy to conduct this research. The population of the study, comprised of managers working in different manufacturing companies in Pakistan, the researchers used an adopted questioner to collect data from respondents. Furthermore, a structural equational model was used to test the hypothesis of the study. The research findings indicate that there is a significant mediating influence of innovative capabilities (IC) in the relationship between big data analytics (BDA) and data availability. This mediating effect has a favourable impact on both sustainable competitive advantage (SCA) and firm performance. This highlights the crucial significance of IC in converting data-derived insights into innovative strategies, thereby contributing to improved competitiveness and the overall performance of the organization. This study is significant for manufacturing organisations and policymakers to make appropriate decisions regarding the usage of big data analytics. This study is limited to manufacturing organisations and selected variables. Future studies may use government policies as a moderator to test the model of current research.

随着空中交通流量增加，终端空域容量及运行效率急剧下降，使航班延误现象严重。本文提出离场延误预测模型，将离场过程分为滑行阶段和排队阶段两部分，分别计算航班的滑行延误和排队延误，从而得到航班的离场总延误。在计算滑行延误的过程中考虑了场面冲突的影响。最后，通过青岛机场的实际延误数据和仿真计算预测的数据对比，表明本文提出的离场延误预测模型能够较为准确的预测航班离场延误值，能为航班运行提供准确依据。 With the increase of air traffic, terminal airspace capacity and operating efficiency dropped sharply, so that flight delays are serious. In this paper, we proposed the departure delay predic-tion model, which divides the departure process into two stages: the taxiing-out stage and the queuing stage. The taxi-out delay and queuing delay are calculated respectively, so as to get the total departure delay. The effect of conflictions is taken into account in the process of calculating taxi-out delays. Finally, through comparing the actual delay data of Qingdao airport and the simulation data, it is shown that the proposed model of departure delay can predict the delay value of flight departure accurately and provide accurate basis for flight operation.

Flight delays represent a significant challenge in the global aviation industry, resulting in substantial costs and a decline in passenger satisfaction. This study addresses the critical issue of predicting flight delays exceeding 15 min using machine learning techniques. The arrival delays at a Turkish airport are analyzed utilizing a novel dataset derived from airport operations. This research examines a range of machine learning models, including Logistic Regression, Naïve Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM. To address the issue of imbalanced data, additional experiments are conducted using the Synthetic Minority Over-Sampling Technique (SMOTE), in conjunction with the incorporation of meteorological data. This multi-faceted approach ensures robust forecast performance under varying conditions. The SHAP (SHapley Additive exPlanations) method is employed to interpret the relative importance of features within the models. The study is based on a three-year period of flight data obtained from a Turkish airport. The dataset is sufficiently extensive and robust to provide a reliable foundation for analysis. The results indicate that XGBoost is the most proficient model for the dataset, demonstrating its potential to deliver highly accurate predictions with an accuracy of 80%. The impact of weather factors on the predictions is found to be insignificant in comparison to scenarios without weather data in this dataset.

PurposeThis paper aims to reduce flight delay propagation, improve flight punctuality rate and ensure aircraft maintenance opportunities by establishing an integrated aircraft scheduling model, aiming at minimizing the total propagated delay and direction operational cost.Design/methodology/approachIn this paper, flight data sets are obtained through automatic dependent detection broadcast. To accurately predict flight delay time, the flight delay prediction eXtreme gradient boosting model adds the data set obtained by random forest advance model learning and predicts the newly generated flight delays. Finally, based on the forecast results, the flight plan can be optimized and adjusted by using the improved column generation algorithm.FindingsIt is verified by the actual weekly planned operation data of an airline company, experiments show that the model established in this paper can reduce flight delay propagation by 30% in case tests and each aircraft has the opportunity to be repaired at the base airport.Originality/valueOptimize the aircraft scheduling plan, cover a wide range of data, not just a single route and airport, supplement the gap in the aircraft scheduling plan based on weather factors to predict flight delays.