- Titel
- Statistics in Air Transportation
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-948337
- Erstveröffentlichung
- 2024
- Datum der Einreichung
- 29.02.2024
- Datum der Verteidigung
- 28.11.2024
- Abstract (EN)
- Civil aviation demands punctual and efficient commercial flights. Flight delays adversely affect passengers, airlines, airports, and the environment (Cook and Tanner, 2015; Cook, Tanner, and Lawes, 2012). Flight delays are typically characterized as the time difference between the actual departure/arrival time of an aircraft and its scheduled departure/arrival time (EUROCONTROL, 2018). Air transportation functions within a complex system and delays are influenced by a multitude of factors. At its core, delays arise due to an imbalance between demand and capacity, where the demand exceeds the available capacity (EUROCONTROL, 2018; Technology Assessment, 1984; Wells and Young, 2004). Air Traffic Flow Management (ATFM) can adjust the demand and balance the imbalance between demand and capacity to achieve a better equilibrium (EUROCONTROL, 2023; Odoni, 1987; Ball et al., 2003; Bertsimas, Lulli, and Odoni, 2011; Murca, 2018; Xu et al., 2020). This dissertation encompasses applications of statistical methods in air transport, such as landing time predictions and weather variable interpolations to enhance ATFM, as well as delay propagation inferences among airports to comprehend patterns of delay transmission, all aiming to understand and mitigate flight delays. Efficient ATFM requires accurate monitoring and prediction of the current capacity and demand imbalance status. Accurate prediction of flight delay helps airports to monitor better, make more informed decisions and increase airport efficiency (Fricke and Schultz, 2009; Lordan, Sallan, and Valenzuela-Arroyo, 2016; Wang et al., 2021). Besides delay prediction, landing time prediction also improves resource monitoring. Many machine learning methods are available to make predictions of landing time. Chapter 2 compares the accuracy of different machine learning methods to predict landing time at Zurich Airport by cross- validation errors. Important factors contributing to the landing time prediction are also identified. The results showcase the effectiveness of the decision tree methods in accurately predicting landing times, which helps improve the management of runways and resources at the local airport. Besides a warning of delays, rerouting can prevent delays by exploring alternative flight routes, which involves re-planning trajectories to bypass congested airspace and hotspots. Weather information serves as a critical input for trajectory planners. The question pertains to choosing interpolation methods to extend the weather data available at 1-degree grid points defined by latitudes, longitudes, and pressure levels with high accuracy. Chapter 3 explores different interpolation techniques for crucial weather variables such as temperature, wind speed, and wind direction. These methods, including Ordinary Kriging, the radial basis function method, neural networks, and decision trees, are compared using cross-validation interpolation errors. A Monte Carlo simulation of a trajectory from Prague to Tunis is conducted to examine the impact of input weather data and the interpolation method (Ordinary Kriging) on planned trajectories. Even though errors in GFS data and Ordinary Kriging are inevitable, the inaccuracy of the data has a minor impact on the planned trajectory. Flight delays negatively affect passengers, airlines, airports, and the environment. Besides mitigating delays at individual airports and for specific flights, considering the potential propagation of delays from other airports is necessary. Assessing delay propagation among airports in the network contributes to understanding the systemic impact of delays. Analyzing delay propagation assists in understanding the patterns of delay transmission and identifying potential strategies for mitigation. Graph network theory has enabled the construction of delay propagation networks to understand the delay transmission pattern using time series data (Belkoura and Zanin, 2016; Zanin, Belkoura, and Zhu, 2017; Du et al., 2018; Mazzarisi et al., 2020b; Xiao et al., 2020; Wang et al., 2020; Jia et al., 2022). However, inferring connections from time series data using statistical methods can introduce biases resulting from excluding airports (Belkoura and Zanin, 2016; Zanin, Belkoura, and Zhu, 2017; Du et al., 2018) or false positives by inappropriate statistical methods (Mazzarisi et al., 2020b), consequently overestimating propagation. Overestimation of delay propagation can undermine the credibility of the reported results, as it becomes dubious to discern whether inaccurate inferences drive the observed delay propagation. Chapter 4 infers Granger causality among airports by avoiding the overestimation of propagation from excluding airports and false positives. The “one-standard-error” rule (Hastie et al., 2009) is recommended to mitigate a high false positive rate during parameter tuning. It is found that the choice of data inputs for model training influences the delay propagation inference results. When early arrivals and punctual flights are included, the observed delay propagation among airports can stem from correlations among punctual and early arrivals rather than delayed flights. In contrast to recent research (Xiao et al., 2020; Jia et al., 2022), this study unveils that large airports exert a substantial influence on the delay propagation network. In summary, this work aims to enhance our understanding of and mitigate flight delays. Chapters 2 and 3 focus on delay mitigation, while Chapter 4 contributes to our understanding of delay interactions.
- Verweis
- Link: https://www.mdpi.com/2504-3900/59/1/5
Using Open Source Data for Landing Time Prediction with Machine Learning Methods
DOI: 10.3390/proceedings2020059005 - Importance of Weather Conditions in a Flight Corridor
Link: https://www.mdpi.com/2571-905X/5/1/18
DOI: 10.3390/stats5010018 - Flight delay propagation inference in air transport networks using the multilayer perceptron
Link: https://www.sciencedirect.com/science/article/pii/S0969699723001539
DOI: 10.1016/j.jairtraman.2023.102510 - Forschungsdatenverweis
- Eurocontrol R&D data archive
Link: http://www.eurocontrol.int/dashboard/rnd-data-archive - airports data
Link: http://openflights.org/data.html - Freie Schlagwörter (EN)
- Granger causality, delay propagation, multilayer perceptron, spatial interpolation
- Klassifikation (DDC)
- 380
- Klassifikation (RVK)
- ZO 7600
- GutachterIn
- Prof. Dr. Ostap Okhrin
- Prof. Dr. Hartmut Fricke
- Dr. Paolo Maranzano
- BetreuerIn Hochschule / Universität
- Prof. Dr. Ostap Okhrin
- Den akademischen Grad verleihende / prüfende Institution
- Technische Universität Dresden, Dresden
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-948337
- Veröffentlichungsdatum Qucosa
- 18.12.2024
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
