Ranking different factors influencing flight delay

The usage of big data analytics is booming today, with its ability to be used to draw useful insights from past data research. Its uses in the aviation industry have a wide array of applications ranging from predicting flight delays to detecting faults in airplane parts. In this paper, we conducted exploratory data analysis on flight dataset to draw inferences on arrival and departure delays and to identify relationships between flight timings and delays. Using the flight delay data, we identified which flight is mostly prone to delays. The arrived upon conclusions are useful for selecting flights in the future.

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.

Flight delays have become a major issue in the aviation industry, impacting operational efficiency and customer satisfaction. This study proposes a CatBoostClassifier-based approach combined with Feature Engineering, Bayesian Optimization, and Random Over Sampling techniques to improve the accuracy of flight delay predictions. Based on model evaluation results, the use of Feature Engineering and Bayesian Optimization enhances performance compared to the baseline CatBoost model. The CatBoost+FE+Bayes combination achieves an accuracy of 83.32%, higher than the unmodified CatBoost model, which only reaches 82.95%. However, applying the Random Over Sampling technique in the CatBoost+FE+Bayes+ROS combination decreases model performance, reducing accuracy to 81.44%. Regarding other metrics, the CatBoost+FE+Bayes model demonstrates the highest F1-score of 0.62, indicating a balance between precision and recall. Additionally, the Area Under Curve (AUC) analysis reveals that CatBoost+FE+Bayes has the highest AUC value of 0.7793, followed by CatBoost+FE at 0.7768, and the unmodified CatBoost model at 0.7643. Meanwhile, the application of ROS leads to a decrease in AUC value to 0.6787. These findings suggest that utilizing Feature Engineering and Bayesian Optimization significantly enhances flight delay predictions. However, resampling techniques such as ROS do not always positively impact the tested model and can even degrade classification performance. The objective of this research is to develop a more accurate flight delay prediction model through the application of appropriate optimization techniques. The resulting model is expected to improve prediction quality and benefit the aviation industry by optimizing operational efficiency and minimizing the negative impact of delays on passengers.

PurposeThe quest to acquire new customers and retain the existing ones is a challenge for all aviation companies. This paper attempts to assist the aviation companies in tackling this challenge by revealing the most important factors that their customers consider to be the most important while making purchasing choices.Design/methodology/approachDrawing on three separate studies, this paper identifies the most important factors that influence consumers' choice of airlines in an emerging economy context. The first study adopts a qualitative approach with 20 respondents (Max QDA is used for analyzing these data), followed by the second study based on a quantitative survey with 105 participants, and finally, the third study which employs a multi-criteria decision-making technique named fuzzy analytic hierarchy process (AHP) on a sample of 12 expert customers.FindingsThe findings of this paper reveal that customer care quality and on-time arrival are the two most important factors that aviation customers consider while making their purchase decisions. In contrast, baggage allowance and fastest route to destination are the two least considered by aviation customers while making their purchasing choices.Research limitations/implicationsThis study includes only a geographic-specific segment of passengers. Future studies of consumer preferences in the aviation industry may include consumers belonging to other geographic regions. Furthermore, future studies may segment their sample populations based on factors other than age, the purpose of travel and booking medium.Practical implicationsThis investigation attempts to give a rich and holistic understanding of the different factors that influence the aviation choices of consumers of varying segments. This study has significant practical implications as modern-day airlines aim to optimally cater to the needs of their target segments without incurring additional costs on services that consumers do not deem to be as important.Originality/valueThis study highlights the importance attributed to each identified factor by passengers, something that previous research has mostly ignored.

Air transportation is a mode of transportation that continues to grow rapidly in Indonesia, marked by the construction of new airports and the improvement of facilities at existing airports. However, flight delays remain one of the main issues faced by the aviation industry, including by Super Air Jet. This study aims to analyze the effect of flight delays on passenger satisfaction at Super Air Jet at Sultan Aji Muhammad Sulaiman Sepinggan International Airport, Balikpapan. The study used a quantitative approach involving 97 respondents who were Super Air Jet passengers. Data management was carried out through several stages of testing, namely instrument testing (validity and reliability), data quality testing (linearity, normality, and heteroscedasticity), and hypothesis testing using simple linear regression analysis, t-test, and coefficient of determination (R²) with the help of IBM SPSS Statistic 22 software. The results of the study indicate that flight duration has a significant effect on passenger satisfaction. This finding emphasizes the importance of improving service quality, especially in efforts to minimize delays, in order to maintain passenger trust in the airline. This study is expected to be a reference for Super Air Jet management in identifying the main causes of flight delays and developing effective strategies to improve passenger satisfaction.

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.

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.

The civil aviation industry is experiencing significant growth in air traffic density within terminal areas, necessitating improved air traffic efficiency. In China’s pursuit of world-class airport clusters, operational complexities arise due to the co-location of these airports in the same terminal area airspace, resulting in lower operational efficiency. To mitigate congestion and flight delays, this study proposes an integrated model that considers multiple runways and route selections, accounting for actual route point restrictions. Utilizing actual operational data from Shanghai metroplex, the proposed model is validated. The study focuses on the airport metroplex system and presents a comprehensive mixed-integer programming (MIP) model for arrival sequencing, considering multiple airports, runways, and routes. The maximum landing efficiency is adopted as the objective function, optimizing arrival scheduling while considering time intervals, route selection, and landing constraints. The Multi-waypoint Rolling Horizon Control (MWRHC) algorithm is employed to tackle time-efficiency challenges, ensuring flight safety by continuous monitoring of flights in the terminal area. Comparative analysis reveals the algorithm’s superior optimization performance for single-runway airports compared to dual-runway airports. Overall, the proposed model and algorithm effectively improve the efficiency of multi-airport arrival scheduling in airport metroplex systems.

Nowadays, the civil aviation industry has a high precision demand of flight delay prediction. To make full use of the characteristics of flight data and meteorological data, two flight delay prediction models using deep convolution neural network based on fusion of meteorological data are proposed in this paper. One is DCNN (Dual-channel Convolutional Neural Network), which refers to the ResNet network structure. The other is SE-DenseNet (Squeeze and Excitation-Densely Connected Convolutional Network), combining the advantages of DenseNet and SENet. Firstly, flight data and meteorological data are fused in the model. Then, both DCNN and SE-DenseNet models are used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is adopted to predict the flight delay level. For proposed DCNN model, both straight channel and convolution channel are designed to guarantee the lossless transmission of the feature matrix and enhance the patency of the deep network. For proposed SE-DenseNet model, a SE module is added after the convolution layer of each DenseNet block, which can not only enhance the transmission of deep information but also achieve feature recalibration in the feature extraction process. The research results indicate that after considering characteristics of meteorological information, the accuracy of the model can be improved 1% compared with only considering the flight information. The two deep convolutional neural networks proposed in this paper, DCNN and SE-DenseNet, can both effectively improve the prediction accuracies, reaching to 92.1% and 93.19%, respectively.

This is quantitative research that study the strategic human resource management of the aviation industry in Thailand in the new normal culture era with objective to study the level of strategic human resource management factors of the Thai aviation industry in the new normal culture era.This is a quantitative research study. The data were collected by using a number of sample questionnaires, which is a ground staff employee of Bangkok Airways.The sample group of employeeswere assigned into 2 groups in order to assess the level of human resource management and the level of competitive advantage of Bangkok Airways. The first group is a group of 310 employees, and the second group is a supervisor and manager, totaling 275 people, a total of 585 people.The data analysis in this research were, Frequency, Percentage, Mean, Standard Deviation and T-test. 2 groups Pearson's Product-moment Correlation Coefficient Analysis and Multiple Regression Analysis.The results of this research shown that Bangkok Airways focusing on the importance of Human Resource Information System (HRIS) at the very most order,From the research result, the researcher recommend that the airline should adopt strategic human resource managementto create a competitive advantage in the aviation industry in Thailand in the new normal culture era in orderto develop the organization's competency in airline business accordingly.

PurposeThis study aims to propose a research model that emphasizes the moderating influence of ethical leadership (ELS) and the link between socially responsible human resource management (SRHRM) and leaders’ eco-helping behavior (LEH) in the aviation industry.Design/methodology/approachThe authors used a time-lag research design. Data were gathered from 397 respondents working for aviation companies in Vietnam and analyzed using SmartPLS 4.0.FindingsThe findings show that SRHRM has a beneficial effect on LEH through employees’ voluntary workplace green behavior (EVB). Based on social cognitive theory, this study developed a theoretical model of how SRHRM influences LEH through EVB. The authors discovered that SRHRM increased LEH and that EVB mediated this favorable relationship. Furthermore, the results demonstrate that ELS mitigates the indirect impact of SRHRM on LEH via EVB.Research limitations/implicationsFuture studies should assess constructs with numerous observations across time, with a larger sample size and in different industrial settings.Practical implicationsVolunteerism is one of the most important values in the aviation industry given that it is vulnerable to practices such as overbooking, delaying, postponing flights and pressure weather.Originality/valueThis study emphasized the impact of SRHRM and ELS on LEH while fulfilling their professional obligations. EVB may be best positioned to mediate the relationship between SRHRM and LEH.

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