baldassarri baronchelli barrat caglioti castellano cattuto complexity dattoli evolutionary_dynamics granular_media granular_media herrmann information_theory innovation_dynamics kreyon language_dynamics loreto mari mukherjee petri pietronero puglisi quantum_optics self_organization servedio statistical_physics techno_social_systems tria zapperi zippers
2015 |
Bernardo Monechi, Vito DP Servedio ; Loreto, Vittorio Congestion Transition in Air Traffic Networks Journal Article PLoS ONE, 10 (5), pp. e0125546, 2015. Abstract | Links | BibTeX | Tag: air traffic, complex_systems, loreto, monechi, servedio, transportation networks @article{Monechi2015, title = {Congestion Transition in Air Traffic Networks}, author = {Bernardo Monechi, Vito DP Servedio and Vittorio Loreto}, url = {http://dx.doi.org/10.1371%2Fjournal.pone.0125546}, doi = {10.1371/journal.pone.0125546}, year = {2015}, date = {2015-05-20}, journal = {PLoS ONE}, volume = {10}, number = {5}, pages = {e0125546}, abstract = {Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios.}, keywords = {air traffic, complex_systems, loreto, monechi, servedio, transportation networks}, pubstate = {published}, tppubtype = {article} } Air Transportation represents a very interesting example of a complex techno-social system whose importance has considerably grown in time and whose management requires a careful understanding of the subtle interplay between technological infrastructure and human behavior. Despite the competition with other transportation systems, a growth of air traffic is still foreseen in Europe for the next years. The increase of traffic load could bring the current Air Traffic Network above its capacity limits so that safety standards and performances might not be guaranteed anymore. Lacking the possibility of a direct investigation of this scenario, we resort to computer simulations in order to quantify the disruptive potential of an increase in traffic load. To this end we model the Air Transportation system as a complex dynamical network of flights controlled by humans who have to solve potentially dangerous conflicts by redirecting aircraft trajectories. The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e.g., the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted. Finally, the generality of the modeling scheme introduced makes it a flexible general tool to simulate and control Air Transportation systems in realistic and synthetic scenarios. |
2012 |
Miguel, Maxi San; Johnson, Jeffrey H; Kertesz, Janosz; Kaski, Kimmo; Diaz-Guilera, Albert; Mackay, Robert S; Loreto, Vittorio; Erdi, Peter; Helbing, Dirk Challenges in complex systems science Journal Article THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS, 214 , pp. 245–271, 2012. Abstract | BibTeX | Tag: complex_systems, diaz-guilera, Erdi, helbing, johnson, kaski, kertesz, loreto, mackay, san miguel, social_dynamics @article{b, title = {Challenges in complex systems science}, author = {Maxi San Miguel and Jeffrey H. Johnson and Janosz Kertesz and Kimmo Kaski and Albert Diaz-Guilera and Robert S. Mackay and Vittorio Loreto and Peter Erdi and Dirk Helbing}, year = {2012}, date = {2012-01-01}, journal = {THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS}, volume = {214}, pages = {245--271}, publisher = {SPRINGER HEIDELBERG}, abstract = {FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda.}, keywords = {complex_systems, diaz-guilera, Erdi, helbing, johnson, kaski, kertesz, loreto, mackay, san miguel, social_dynamics}, pubstate = {published}, tppubtype = {article} } FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda. |
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