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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.

 

Systemic delay propagation in the US airport network

Pablo Fleurquin, Jose J. Ramasco, Victor M. Eguiluz

http://arxiv.org/abs/1301.1136

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The ultimate guide to memory - New Scientist

We are all collections of memories. They dictate how we think, act and make decisions, and even define our identity.

Yet memory, with its many virtues and flaws, has puzzled for centuries. How are memories made and stored in the brain? Why do we remember some events but not others? What do other animals remember? And how can we improve the flawed instrument handed to us by evolution?

In these articles we answer these questions and many more, starting with a revolutionary new understanding of memory’s purpose.


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Complex dynamics of elementary cellular automata emerging in chaotic rules

Complex dynamics of elementary cellular automata emerging in chaotic rules | Papers | Scoop.it

We show novel techniques of analysing complex dynamics of cellular automata (CA) with chaotic behaviour. CA are well known computational substrates for studying emergent collective behaviour, complexity, randomness and interaction between order and disorder. A number of attempts have been made to classify CA functions on their spatio-temporal dynamics and to predict behavior of any given function. Examples include mechanical computation, lambda and Z-parameters, mean field theory, differential equations and number conserving features. We propose to classify CA based on their behaviour when they act in a historical mode, i.e. as CA with memory. We demonstrate that cell-state transition rules enriched with memory quickly transform a chaotic system converging to a complex global behaviour from almost any initial condition. Thus in just a few steps we can select chaotic rules without exhaustive computational experiments or recurring to additional parameters. We provide analysis of well-known chaotic functions in one-dimensional CA, and decompose dynamics of the automata using majority memory.

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