scholarly journals Causal Discovery of Flight Service Process Based on Event Sequence

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Qian Luo ◽  
Lin Zhang ◽  
Zhiwei Xing ◽  
Huan Xia ◽  
Zhao-Xin Chen
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Civil Aviation ◽  
Causal Discovery ◽  
Aviation Industry ◽  
Service Process ◽  
Ideal Model ◽  
Flight Delays ◽  
Causal Factors ◽  
Ground Support

The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.

Importance of Personal Data Protection Law for Commercial Air Transport

2017 ◽  
Vol 2017 (1) ◽  
pp. 35-44
Author(s):  
Dawid Zadura
Keyword(s):  
Data Protection ◽  
Public Information ◽  
Personal Data ◽  
Air Transport ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Personal Data Protection ◽  
It Systems ◽  
Data Protection Law ◽  
The Law

Abstract In the review below the author presents a general overview of the selected contemporary legal issues related to the present growth of the aviation industry and the development of aviation technologies. The review is focused on the questions at the intersection of aviation law and personal data protection law. Massive processing of passenger data (Passenger Name Record, PNR) in IT systems is a daily activity for the contemporary aviation industry. Simultaneously, since the mid- 1990s we can observe the rapid growth of personal data protection law as a very new branch of the law. The importance of this new branch of the law for the aviation industry is however still questionable and unclear. This article includes the summary of the author’s own research conducted between 2011 and 2017, in particular his audits in LOT Polish Airlines (June 2011-April 2013) and Lublin Airport (July - September 2013) and the author’s analyses of public information shared by International Civil Aviation Organization (ICAO), International Air Transport Association (IATA), Association of European Airlines (AEA), Civil Aviation Authority (ULC) and (GIODO). The purpose of the author’s research was to determine the applicability of the implementation of technical and organizational measures established by personal data protection law in aviation industry entities.

scholarly journals Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

2021 ◽  
Vol 3 (2) ◽  
pp. 392-413
Author(s):  
Stefan Studer ◽  
Thanh Binh Bui ◽  
Christian Drescher ◽  
Alexander Hanuschkin ◽  
Ludwig Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Practical Experience ◽  
Special Focus ◽  
Close Monitoring ◽  
Machine Learning Applications ◽  
Project Organizations ◽  
Considerable Impact ◽  
Learning Development

Machine learning is an established and frequently used technique in industry and academia, but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners face manifold challenges and risks when developing machine learning applications and have a need for guidance to meet business expectations. This paper therefore proposes a process model for the development of machine learning applications, covering six phases from defining the scope to maintaining the deployed machine learning application. Business and data understanding are executed simultaneously in the first phase, as both have considerable impact on the feasibility of the project. The next phases are comprised of data preparation, modeling, evaluation, and deployment. Special focus is applied to the last phase, as a model running in changing real-time environments requires close monitoring and maintenance to reduce the risk of performance degradation over time. With each task of the process, this work proposes quality assurance methodology that is suitable to address challenges in machine learning development that are identified in the form of risks. The methodology is drawn from practical experience and scientific literature, and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support, but fails to address machine learning specific tasks. The presented work proposes an industry- and application-neutral process model tailored for machine learning applications with a focus on technical tasks for quality assurance.

scholarly journals Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 152
Author(s):  
Micha Zoutendijk ◽  
Mihaela Mitici
Keyword(s):  
Assignment Problem ◽  
Absolute Error ◽  
Aviation Industry ◽  
Probabilistic Forecasting ◽  
Flight Delays ◽  
Mixture Density ◽  
Assignment Model ◽  
Gate Assignment ◽  
Flight Delay ◽  
Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

scholarly journals Modelling of the Impact of National Standardization on Duopolistic Competition in the Civil Aircraft Market

2020 ◽  
Vol 17 (4) ◽  
pp. 859-873
Author(s):  
E. V. Varyukhina ◽  
◽  
V. V. Klochkov ◽  
Keyword(s):  
Market Competition ◽  
Civil Aviation ◽  
Aviation Industry ◽  
Equilibrium Degree ◽  
Aviation Noise ◽  
Market Shares ◽  
Civil Aircraft ◽  
Zero Sum ◽  
The Cost ◽  
The Impact

The purpose of this study is to analyze standards as one of the main tools of protectionism in global markets of industrial goods. We use standards for modeling of market competition and adapt this approach for civil aviation markets. The role of local noise standards in civil aircraft markets is discussed. Imposition of more stringent aviation noise standards is modelled in the form of a two-person non-zero-sum game. Players are aircraft corporations that conduct research and development to reduce noise and lobby for stricter regulations in their controlled markets. The model can be used to predict that tighter aviation noise standards will be imposed and to justify the strategy of Russian aviation industry and science. The proposed approach can be adapted for other industries with strict regulations (in terms of safety, ecology). Such estimation allows us to assess whether it is in the country’s interests to participate in the standards race or not. It is shown that the equilibrium degree of standards tightening is higher if the players’ market shares are close to equal or individual players have advantages in the cost of production and/or product improvement is highly likely due to the company’s R&D progress.

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