Data Analysis of Delays in Airline Networks

Cost-optimized airline resource schedules often imply a lack of delay tolerance in case of unforeseen disruptions, e.g. late check-ins, technical defects or airport and airspace congestion. Therefore, the consideration of timeliness and robustness has become an important topic in robust resource scheduling and a wide range of sophisticated scheduling approaches has been developed in recent years. However, these approaches depend on assumptions made concerning delay occurrences. A better understanding of delay mechanisms may lead to a better trade-off between cost-efficiency and robustness and is therefore the purpose of this paper. We provide a data-driven detection of decision rules for daytime delay trends, depending on spatio-temporal attributes. The focus is on interpretable rules whose prediction accuracy is compared to random forests as a non-parametric, automated modeling approach. The obtained results give an insight into both the nature of primary delay occurrence and the methodical potential of delay prediction in the context of robust resource scheduling

Analyse des incertitudes dans les flux du trafic aérien.

Air-traffic management (ATM) consists of a predictive component (traffic planning) and an adaptive component (traffic control). The aim of the predictive component is to balance airspace demand with available capacity. The adaptive component has to guide aircraft safely to their destinations, once they are in the air. Uncertainties (e.g delay from connecting flights, technical failure) create the phenomenon of gaps between the predictive and the adaptive component. This causes safety problems and non-optimally used capacity. While the main sources of uncertainties are identified (demand uncertainties, capacity uncertainties, flow control uncertainties) the mechanisms of how they affect the components of air-traffic management remain unknown. Our approach is to analyze past flight data to generate hypotheses about the mechanisms that lead to gaps between the predictive and the adaptive component in ATM. This is a pragmatic first step in the analysis of a physical phenomenon. It is based on probability theory and more precisely on a frequentist interpretation of uncertainty. We use multivariate data analysis techniques and stochastic Point processes to infer new knowledge about the phenomenon. Our main results are i, there are systematic gaps in each sector evaluated. The size of these gaps can be characterized by Poisson distributions and there is a systematic shift to suppress traffic at high planned levels. This is counter-intuitive because one expects that the different uncertainty factors cancel out in average. We then prove that random disturbances of an arrival process cause systematic gaps in three classes of flight schedules. We conclude that even if all controllable uncertainties in flow planning were eliminated, systematic gaps between the number of planned and realized traffic would remain. This result is useful in tactical flow planning. New constraints in the slot-allocation procedure can be found by identifying classes of flight schedules that are robust to random disturbance. ii, we show that gaps propagate exclusively on flight routes. No unexpected propagation is identified. This is evidence that no systematic re-routing is initiated by controllers to absorb gaps. We also identify high tail probabilities and two time-series models which describe the second-order characteristics of the process that disturbs the flight schedules. This is evidence that the disturbances are heterogeneous and not independent. This result is empirical and we conjecture that the observed behavior is due to aggregation and long-range dependence at the sector level. As future work we propose to continue the identification of classes of flight schedules that absorb the impact of uncontrollable disturbances and to develop statistical models that explain long-term congestion patterns. This is a basis to quantify the impact of local decisions on the performance of the global sector network.La gestion du trafic aerien (air-traffic management, ATM) consiste en une composante predictive (planification du trafic) et en une composante adaptative (controle du trafic). Le but de la composante predictive est de trouver un equilibre entre la demande de l'espace et la capacite disponible. Une fois que les avions ont decolle, la composante adaptative doit les guider en toute securite vers leurs destinations. Des incertitudes, telles que retards ou defaillances techniques, creent des phenomenes d'ecarts entre la composante predictive et adaptative. Cela entraine des problemes de securite ainsi qu'une utilisation sous-optimale de capacite. Meme si les causes majeures des incertitudes sont connues (incertitude de demande, incertitude de capacite, incertitude de gestion de flux), les mecanismes perturbateurs restent inconnus. L'approche de cette these est d'analyser des donnees d'ecoulement de trafic afin d'engendrer de nouvelles hypotheses sur les mecanismes qui causent des ecarts entre la composante predictive et adaptative dans l'ATM. C'est un premier pas pragmatique dans l'analyse d'un phenomene physique. Il est fonde sur le calcul des probabilites et plus precisement sur l'interpretation frequentiste des probabilites. On utilise des techniques d'analyse de donnees multi-variees et des processus ponctuels stochastiques afin d'inferer de nouvelles connaissances sur le phenomene. Nos resultats principaux sont: (i) des ecarts systematiques existent dans tous les secteurs evalues. Leur taille peut etre caracterisee par des distributions de Poisson et on constate une tendance systematique a supprimer le trafic sur des niveaux eleves de planification. C'est un resultat contre-intuitif car l'on s'attend a ce que les differents facteurs d'incertitude s'annullent en moyenne. Ensuite on montre que des perturbations aleatoires d'un processus d'arrivee causent des ecarts systematiques dans deux classes de plan de vol. On conclut que meme si toutes les incertitudes controlables etaient eliminees, des ecarts systematiques entre le nombre planifie et observe de vols apparaisseraient. Ce resultat est utile pour la planification tactique des flux. De nouvelles contraintes pour le probleme de l'allocation de creneaux peuvent etre formulees en identifiant des plans de vol qui sont robustes aux perturbations aleatoires. (ii) on montre que les ecarts se propagent uniquement le long des routes aeriennes. Aucune propagation non-attendue n'est identifiee. Cela indique que les controlleurs aeriens n'utilisent pas systematiquement le re-routage pour compenser les ecarts. On remarque egalement des probabilites de queue elevees et on propose deux (nouveaux) modeles de series chronologiques qui decrivent les caracteristiques du processus de perturbation des plans de vol. Cela indique que les perturbations sont d'une nature heterogene et non-independante. Le resultat est empirique et on affirme que le comportement observe est du a des dependances entre les avions intervenant sur un long-terme. Comme travaux futurs on propose de continuer l'identification des ordonnancements de vol qui absorbent l'impact des incertitudes non-controlables et de developper des modeles statistiques qui expliquent les echantillons long-terme de congestion. Ceci constitue une base pour la quantifiaction de l'impact des decisions locales sur la performance globale du reseau de transport

Characteristics in flight data - estimation with logistic regression and support vector machines

We analyze data from flight sectors. The questions are whether there are differences between weekend and weekdays and among sectors. We compare expected prediction errors of linear logistic regression and of linear and non linear kernel classifiers. Linear decision boundaries impose an average prediction error of around around 26 % for the weekend data and around 15 % for the sector name data. Non linear boundaries do not improve the predictive accuracy by more than 4 %. Thus, there is some characteristic in the data which is identified by both methods. General Background Airspace is divided into geographical regions