Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accordingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, the authors analyze the benefits of carpooling from a new angle, posing it as a possible means for favoring social integration in the city by matching carpoolers taking into account some of their social characteristics. Building upon a recently introduced network-based approach to model ride-sharing opportunities, the authors define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, the authors provide corresponding optimal and computationally efficient solutions. The authors then demonstrate their approach on two data sets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.

Source: trid.trb.org

 

“Stimulating competition is one of the main topics in most health care reform debates, and it has been a central issue in the Affordable Care Act in the United States since 2009. The goal of this paper is to use complex network methods to study dynamic and structure of competition under Affordable Care Act (ACA) and its evolution over time since its beginning until 2017. Using publicly available data, we construct a bipartite network of counties and insurance providers, create associated weighted single-mode networks, and analyze the evolution of network parameters that are related to competition and potential collusion in complex networks. These parameters have been previously tied to dynamics of collaboration and competition in earlier theoretical works. We argue that three parameters, namely network modularity, and eigenvector centrality mean and skewness are appropriate indicators of the overall competition in the insurance market. Based on these parameters, we show that the level of systemic competition among insurers as a function of time is an inverse U-shape trend, and that competition has returned back to what it was at the very beginning of ACA, indicating an undesirable resilience in the national health care system.”

 

Dynamic Structure of Competition Networks in Affordable Care Act Insurance Market

David A Gianetto;  Mohsen Mosleh;  Babak Heydari

IEEE Access

DOI: 10.1109/ACCESS.2018.2800659

Source: ieeexplore.ieee.org

Although networks are extensively used to visualize information flow in biological, social and technological systems, translating topology into dynamic flow continues to challenge us, as similar networks exhibit fundamentally different flow patterns, driven by different interaction mechanisms. To uncover a network’s actual flow patterns, here we use a perturbative formalism, analytically tracking the contribution of all nodes/paths to the flow of information, exposing the rules that link structure and dynamic information flow for a broad range of nonlinear systems. We find that the diversity of flow patterns can be mapped into a single universal function, characterizing the interplay between the system’s topology and its dynamics, ultimately allowing us to identify the network’s main arteries of information flow. Counter-intuitively, our formalism predicts a family of frequently encountered dynamics where the flow of information avoids the hubs, favoring the network’s peripheral pathways, a striking disparity between structure and dynamics.

 

Dynamic patterns of information flow in complex networks
Uzi Harush & Baruch Barzel
Nature Communicationsvolume 8, Article number: 2181 (2017)
doi:10.1038/s41467-017-01916-3

Source: www.nature.com

Interaction patterns in human communication networks are characterized by intermittency and unpredictable timing (burstiness). Simulated spreading dynamics through such networks are slower than expected. A technology for automated recording of social interactions of individual honeybees, developed by the authors, enables one to study these two phenomena in a nonhuman society. Specifically, by analyzing more than 1.2 million bee social interactions, we demonstrate that burstiness is not a human-specific interaction pattern. We furthermore show that spreading dynamics on bee social networks are faster than expected, confirming earlier theoretical predictions that burstiness and fast spreading can co-occur. We expect that these findings will inform future models of large-scale social organization, spread of disease, and information transmission.

 

Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks
Tim Gernat, Vikyath D. Rao, Martin Middendorf, Harry Dankowicz, Nigel Goldenfeld and Gene E. Robinson
PNAS 2018; published ahead of print January 29, 2018, https://doi.org/10.1073/pnas.1713568115

Source: www.pnas.org

Source: www.mitpressjournals.org

See Also: Special Issue articles on Early Access: https://www.mitpressjournals.org/toc/artl/0/0