Abstract: Information technologies today can inform drivers about their alternatives for shortest paths from origins to destinations, but they do not contain incentives or information that promote collective benefits. To obtain such benefits, we need to have not only good estimates of how the traffic is formed but also to have target strategies to reduce enough vehicles from the best possible roads in a feasible way. Moreover, to reach the target vehicle reduction is not trivial, it requires individual sacrifices such as drivers taking alternative routes, shifts in departure times or even changes in modes of transportation. The opportunity is that during large events (Carnivals, Festivals, Races, etc.) the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for social good. In this paper, we integrate for the first time big data resources to quantify the impact of events and propose target strategies for collective good at urban scale. In the context of the Olympic Games in Rio de Janeiro, we first predict the increase in traffic by integrating data from: mobile phones, schedules and venues of sport matches, Airbnb, Waze and transit information. Second, we evaluate the impact of Olympic Games to the travel of commuters, and propose different route choice scenarios during the morning and evening peak hours. Moreover, we pinpoint the trips which have greatest contribution to the global congestion and propose reasonable collective reduction. Interestingly, we show that (i) following new route options with individual shortest path can save more collective travel time than keeping routine routes, uncovering the positive value of information technologies during events; (ii) if a small targeted proportion of people from specific areas switch from driving to public transport, the collective travel time can be reduced to a great extent.

Abstract: Multilayer networks provide a natural framework for studying transportation systems. Within this approach I will show on practical examples how we can understand the structure of these systems and quantify the impact of the coupling between different modes. I will also discuss how a simple dynamical model on these systems allows to explain some statistical patterns observed for human mobility.

Abstract: To connect structure and dynamics in interconnected systems, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of elements with multiple layers of interactions. The information-theoretic and flow-based method known as the map equation was recently generalized to multilayer networks. However, it has been unclear how to further generalize the method to any type of layered network, such as multiplex networks, interconnected multiplex networks, interdependent networks, and interconnected multilayer networks. Here we show that discriminating between physical nodes for describing the concrete elements that flow entities can visit and abstract state nodes for representing the dynamics allows for completely generalized mapping. The generalized mapping framework applies to dynamics in multilayer and interconnected systems as well as any higher-order Markov chain model. We demonstrate how representations that are true to the system at hand provide the most effective analysis.

Abstract: Dynamical processes on complex networks has been an active research area over the past decade. In this talk, we will present recent results on two major and related classes of dynamical processes: i) Information propagation, also known as simple contagion, and ii) Influence propagation, also known as complex contagion. With regard to simple contagions, we will consider a clustered multi-layer network model to capture the fact that information may propagate simultaneously over multiple social networks. Assuming that information propagates according to the SIR model and with different information transmissibility across the networks, we give results for the conditions, probability, and size of information epidemics. We present analogous results for complex contagions over clustered multiplex networks under a generalized linear threshold model. Collecting, we demonstrate several non-trivial results concerning the impact of clustering on contagion dynamics. Last but not least, we compare the dynamics of complex contagions over multiplex networks and their monoplex projections and demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.

Abstract: The airline transportation system is a paradigmatic example of multiplex network, where nodes represent airports, edges stand for direct flights between two locations, and each layer contains all the routes operated by the same carrier. In this work we propose a genuinely multiplex model of network growth, based on a trade-off between the maximisation of the number of potential travellers and the minimisation of competition on each route. By using real data about the six continental air transportation networks, we show that the model is able to reproduce quite accurately the structural properties of these systems, and in particular the observed patterns of egde overlap and node activity distribution. The results suggest that each airline really tends to organise its network in order to optimise a trade-off between efficiency and competition, and that the networks of all the airlines of a continent are indeed placed very close to the theoretical Pareto front in the efficiency-competition plane. We finally explain how this simple model can be used to suggest which routes should be added to an existing network in order to improve the overall performance of an airline. This work sheds light on the fundamental role played by multiplexity in shaping the structure of continental air transportation systems, and provides new interesting insight about effective strategies for network optimisation based exclusively on structural considerations.

Abstract: In the last decade or so, the study of interconnected complex networks gained much interest across the scientific community. In neuroscience, in particular, systems are often composed of many networks that interact on different spatial and temporal scales. This may result in activity that is synchronized within and/or across networks, in its simplest form in-phase oscillations. We studied numerically the synchronizability of two interconnected oscillator networks of finite-size. In contrast to other studies that assessed synchronizability by the asymptotic state of (local) Kuramoto order parameter, we quantified is by the (local) relaxation time towards that asymptotic state. In view of finite-size effects, the latter was expected to be quite erratic which motivated using a statistical approach often employed to quantify stochastic dynamics: We determined the serial-lag auto-correlation function of the simulated time series of the networks? order parameters. The envelope of the auto-correlation function decayed exponentially (as in the case of a linear response system), which allowed for estimating the relaxation time. We first tested procedures in the case of two fully connected, symmetric networks and compared. Our numerical estimates of the relaxation times closely resembled the analytically known bifurcation scheme. Next we changed the topology of one of the networks to be random (Erd?s-R?nyi), scale free (Barab?si-Albert) and small world (Newman-Watts) by guarantying that the asymptotic value of local synchronization in that that network remained constant. Dependent of the relation of average degree and within-network vis-?-vis between-network coupling strength, topology transferred from one network to the other. Quantifying synchronizability through the (local) relaxation time appears a useful tool when it comes to interactions between oscillator networks.