Information technologies today can inform each of us about the route with the shortest time, but they do not contain incentives to manage travellers such that we all get collective benefits in travel times. To that end we need travel demand estimates and target strategies to reduce the traffic volume from the congested roads during peak hours in a feasible way. During large events, the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for collective benefits in traffic. In this paper, we integrate, for the first time, big data resources to estimate the impact of events on traffic and propose target strategies for collective good at the urban scale. In the context of the Olympic Games in Rio de Janeiro, we first predict the expected increase in traffic. To that end, we integrate data from mobile phones, Airbnb, Waze and transit information, with game schedules and expected attendance in each venue. Next, we evaluate different route choice scenarios for drivers during the peak hours. Finally, we gather information on the trips that contribute the most to the global congestion which could be redirected from vehicles to transit. Interestingly, we show that (i) following new route alternatives during the event with individual shortest times can save more collective travel time than keeping the routine routes used before the event, uncovering the positive value of information technologies during events; (ii) with only a small proportion of people selected from specific areas switching from driving to public transport, the collective travel time can be reduced to a great extent. Results are presented online for evaluation by the public and policymakers

 

Collective benefits in traffic during mega events via the use of information technologies
Yanyan Xu, Marta C. González
Published 12 April 2017.DOI: 10.1098/rsif.2016.1041

Royal Society Interface

April 2017
Volume 14, issue 129

Source: rsif.royalsocietypublishing.org

Energy saving mechanisms in nature allow following organisms to expend less energy than leaders. Queues, or ordered rows of individuals, may form when organisms exploit the available energy saving mechanism while travelling at near-maximal sustainable metabolic capacities; compact clusters form when group members travel well below maximal sustainable metabolic capacities. The group size range, given here as the ratio of the difference between the size of the largest and smallest group members, and the size of the largest member (as a percentage), has been hypothesized to correspond proportionately to the energy saving quantity because weaker, smaller, individuals sustain the speeds of stronger, larger, individuals by exploiting the energy saving mechanism (as a percentage). During migration, small individuals outside this range may perish, or form sub-groups, or simply not participate in migratory behaviour. We approximate drag forces for leading and following individuals in queues of the late Devonian (c. 370 Ma) trilobite Trimerocephalus chopini. Applying data from literature on Rectisura herculea, a living crustacean, we approximate the hypothetical walking speed and maximal sustainable speeds for T. chopini. Our findings reasonably support the hypothesis that among the population of fossilized queues of T. chopini reported in the literature, trilobite size range was 75%, while the size range within queues was 63%; this corresponds reasonably with drag reductions in following positions that permit c. 61.5% energy saving for trilobites following others in optimal low-drag positions. We model collective trilobite behaviour associated with hydrodynamic drafting.

 

Trilobite ‘pelotons’: possible hydrodynamic drag effects between leading and following trilobites in trilobite queues
Hugh Trenchard, Carlton E. Brett, Matjaž Perc

Palaeontology

Volume 60, Issue 4
July 2017
Pages 557–569

10.1111/pala.12301

Source: onlinelibrary.wiley.com

While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure.

 

Efficient method for estimating the number of communities in a network
Maria A. Riolo, George T. Cantwell, Gesine Reinert, M. E. J. Newman

Source: arxiv.org

We introduce a network growth model based on complete redirection: a new node randomly selects an existing target node, but attaches to a random neighbor of this target. For undirected networks, this simple growth rule generates unusual, highly modular networks. Individual network realizations typically contain multiple macrohubs—nodes whose degree scales linearly with the number of nodes N. The size of the network “nucleus”—the set of nodes of degree greater than one—grows sublinearly with N and thus constitutes a vanishingly small fraction of the network. The network therefore consists almost entirely of leaves (nodes of degree one) as N→∞.

 

Emergent Network Modularity
P. L. Krapivsky, S. Redner

Source: arxiv.org

New math shows how, contrary to conventional scientific wisdom, conscious beings and other macroscopic entities might have greater influence over the future than does the sum of their microscopic components.

Source: www.quantamagazine.org