Head of Data
List of publications in peer-review journals
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.
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.
Complex 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 paying 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.
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.
Recent works in the area of Complex Systems have addressed the robustness of networks such as power grids, social groups and the Internet. The robustness is evaluated against an external perturbation that can be different in nature depending on the particular network. For instance, the failure of power transmission lines that can trigger a nationwide blackout or a general shutdown of routers and the consequent connectivity loss. In this work, we introduce metrics inspired by Complexity Science to explore the robustness of the air transportation system in the US with respect to delay propagation. We use an agentbased model recently developed to simulate delay propagation and assess the effect of disruptions in the network. These disruptions are introduced as initial conditions and can affect single flights or full airports. The model is then run with and without disruptions and the outcome is compared to quantify the system robustness. Our results indicate that large hubs (in the sense of number of offered destinations) are more vulnerable to flight delays than small or medium sized airports. However, the impact in the whole network of delays initiated in an airport does not depend on whether it is a hub or not. We also detect a set of high impact flights and explore the drivers that generate these long tail extreme events.
Complex Systems are those in which a very large number of elements interact, usually in a non-linear fashion, producing emergent behaviors that are typically difﬁcult to predict. Air transportation systems fall in this category, with a large number of aircraft following a pre-scheduled program. Network and airline managers, passengers, crews and airport staffs are involved in the daily operations and may suffer the consequences when failures in the system such as delays appear. It has been shown that it is possible to understand and forecast delays propagation in these systems. In the framework of SESAR WP-E TREE project, we have developed a model for characterizing and forecasting the spreading of reactionary delays through the European Network. Our results are preliminary, but show a promising agreement with empirical ﬂight performance data.
En este trabajo, nos centraremos en las redes de movilidad y transporte. Pasaremos desde las redes de movilidad en ambientes urbanos, tal y como se pueden observar gracias a las nuevas tecnologías de la información, a redes de conexión entre ciudades y, más en concreto, a un análisis de la red de transporte aéreo. Estos resultados forman parte de un contexto mucho más amplio, pero dan una idea del estado actual de esta disciplina.