Data-driven modeling of systemic delay propagation under severe meteorological conditions

The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large-scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delay-spreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.Comment: 9 pages, 5 figures. Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013

Systemic propagation of delays in the air-transportation network

Tesis doctoral presentada por Pablo Fleurquin para optar al título de Doctor, en el Programa de Física del Departamento de Física de la Universitat de les Illes Balears, realizada en el IFISC bajo la dirección de José Javier Ramasco Sukia, Víctor Martinez Eguíluz y como ponente Maxi San Miguel Ruibal.[EN] The focus of this dissertation is to quantitative describe, analyze and model a paradigmatic socio-technical complex system such as the air-transportation system. The generation, propagation and eventual amplification of flight de- lays involve a large number of interacting mechanisms. Such mechanisms can be classified as internal or external to the air tra c system. The basic internal mechanisms include aircraft rotations (the di erent flight legs that comprise an aircraft itinerary), airport operations, passengers’ connections and crew rotation. In addition, external factors, such as weather perturbations or security threats, disturb the system performance and contribute to a high level of system-wide congestion. Although this socio-technical system is driven by human decisions, the intricacy of the interactions between all these elements calls for an analy- sis of flight delays under the scope of Complex Systems theory. Complexity is concerned with the emergence of collective behavior from the microscopic interaction of the system elements. Several tools have been developed to tackle complexity. Here we use Complex Networks theory and take a system-wide perspective to broaden the understanding of delay propagation.Peer reviewe

Data-driven modeling of systemic delay propagation under severe meteorological conditions

Trabajo presentado en el 10th USA/Europe Air Traffic Management Research and Development Seminar (2013), celebrado en Chicago del 10 al 13 de junio de 2013.The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large- scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delay-spreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.PF receives support from the network Complex World within the WPE of SESAR (Eurocontrol and EU Commission). JJR acknowledges funding from the Ramón y Cajal program of the Spanish Ministry of Economy (MINECO). Partial support from MINECO and FEDER was received through projects MODASS (FIS2011-24785), FISICOS (FIS2007-60327) and INTENSE@COSYP (FIS2012-30634). Funding was also received from the EU Commission through projects EUNOIA FP7-DG.Connect-318367) and LASAGNE (FP7-ICT-318132).Peer reviewe

Negentropy Generation and Fractality in the Dry Friction of Polished Surfaces

We consider the Robin Hood model of dry friction to study entropy transfer during sliding. For the polished surface (steady state) we study the probability distribution of slips and find an exponential behavior for all the physically relevant asperity interaction-distance thresholds. In addition, we characterize the time evolution of the sample by its spatial fractal dimension and by its entropy content. Starting from an unpolished surface, the entropy decreases during the Robin Hood process, until it reaches a plateau; thereafter the system fluctuates above the critical height. This validates the notion that friction increases information in the neighborhood of the contacting surface at the expense of losing information in remote regions. We explain the practical relevance of these results for engineering surface processing such as honing

Systemic delay propagation in transportation networks

Master in Physics at the Universitat de les Illes Balears.The Science of Complexity is currently undergoing a rapid expansion through a va- riety of interdisciplinary elds such as computer science, technology, biology and socio-economical systems. Complexity Science is a new approach to comprehend a certain type of systems, the so-called Complex Systems, composed of a large number of interacting entities that produce emergent behaviors in the system. Given this general framework, Complexity is not used to refer just to complicated phenom- ena within Science, it emphasizes the notion of collective behavior that surge from microscopic interaction scales. Statistical Mechanics has provided a mathematical toolbox to analyze Complex Systems. Thus, modeling approaches from physics are combined with powerful computational resources to gain insight on fundamental questions poorly suited to traditional science.Peer reviewe

Characterization of delay propagation in the US air-transportation network

arXiv:1304.2528Complex networks provide a suitable framework to characterize air traffic. Previous works described the world air-transport network as a graph where direct flights are edges and commercial airports are vertices. In this work, we focus instead on the properties of flight delays in the US air-transportation network. We analyze flight performance data in 2010 and study the topological structure of the network as well as the aircraft rotation. The properties of flight delays, including the distribution of total delays, the dependence on the day of the week and the hour-by-hour evolution within each day, are characterized with special attention to flights accumulating delays longer than 12 hours. We find that the distributions are robust to changes in takeoff or landing operations, different moments of the year, or even different airports in the contiguous states. However, airports in remote areas (Hawaii, Alaska, Puerto Rico) can show peculiar distributions biased toward long delays. Additionally, we show that long-delayed flights have an important dependence on the destination airport. © 2014 The Pennsylvania State University, University Park, PA.J. Ramasco is funded by the Ramón y Cajal program of the Spanish Ministry of Economy and Competitiveness (MINECO). Partial support was also received from MINECO (Spain) and FEDER (EU) through the project MODASS (FIS2011-24785) and from the EU Commission through the FP7 projects EUNOIA and LASAGNE.N

Método para caracterizar la congestión aeroportuaria en una red de tráfico aéreo

Método para pronosticar la congestión aeroportuaria en una red de tráfico aéreo que combina tanto un algoritmo basado en colas de espera, como las relaciones entre vuelos propias de la planificación de cada aerolínea y que se caracteriza porque establece una simulación basada en la rotación de cada aeronave, los vuelos en conexión, congestión aeroportuaria y retrasos aleatorios; y donde un quinto factor simula las perturbaciones externas; y donde además se establece un proceso de gestión de los aeropuertos independiente del proceso de simulaciónPeer reviewedUniversitat de les Illes BalearsA1 Solicitud de patente con informe sobre el estado de la técnic

Systemic delay propagation in the US airport network

Technologically driven transport systems are characterized by a networked structure connecting operation centers and by a dynamics ruled by pre-established schedules. Schedules impose serious constraints on the timing of the operations, condition the allocation of resources and define a baseline to assess system performance. Here we study the performance of an air transportation system in terms of delays. Technical, operational or meteorological issues affecting some flights give rise to primary delays. When operations continue, such delays can propagate, magnify and eventually involve a significant part of the network. We define metrics able to quantify the level of network congestion and introduce a model that reproduces the delay propagation patterns observed in the U.S. performance data. Our results indicate that there is a non-negligible risk of systemic instability even under normal operating conditions. We also identify passenger and crew connectivity as the most relevant internal factor contributing to delay spreading.PF receives support from the network Complex World within the WPE of SESAR (Eurocontrol and EU Commission). JJR acknowledges funding from the Ramón y Cajal program of the Spanish Ministry of Economy (MINECO). Partial support from MINECO and FEDER was also received through projects MODASS (FIS2011-24785) and FISICOS (FIS2007-60327), and from the EU Commission through projects EUNOIA (FP7-DG.Connect-318367) and LASAGNE (FP7-ICT-318132).Peer Reviewe