148 research outputs found
High-Resolution Road Vehicle Collision Prediction for the City of Montreal
Road accidents are an important issue of our modern societies, responsible
for millions of deaths and injuries every year in the world. In Quebec only, in
2018, road accidents are responsible for 359 deaths and 33 thousands of
injuries. In this paper, we show how one can leverage open datasets of a city
like Montreal, Canada, to create high-resolution accident prediction models,
using big data analytics. Compared to other studies in road accident
prediction, we have a much higher prediction resolution, i.e., our models
predict the occurrence of an accident within an hour, on road segments defined
by intersections. Such models could be used in the context of road accident
prevention, but also to identify key factors that can lead to a road accident,
and consequently, help elaborate new policies.
We tested various machine learning methods to deal with the severe class
imbalance inherent to accident prediction problems. In particular, we
implemented the Balanced Random Forest algorithm, a variant of the Random
Forest machine learning algorithm in Apache Spark. Interestingly, we found that
in our case, Balanced Random Forest does not perform significantly better than
Random Forest.
Experimental results show that 85% of road vehicle collisions are detected by
our model with a false positive rate of 13%. The examples identified as
positive are likely to correspond to high-risk situations. In addition, we
identify the most important predictors of vehicle collisions for the area of
Montreal: the count of accidents on the same road segment during previous
years, the temperature, the day of the year, the hour and the visibility
Modeling the dynamical interaction between epidemics on overlay networks
Epidemics seldom occur as isolated phenomena. Typically, two or more viral
agents spread within the same host population and may interact dynamically with
each other. We present a general model where two viral agents interact via an
immunity mechanism as they propagate simultaneously on two networks connecting
the same set of nodes. Exploiting a correspondence between the propagation
dynamics and a dynamical process performing progressive network generation, we
develop an analytic approach that accurately captures the dynamical interaction
between epidemics on overlay networks. The formalism allows for overlay
networks with arbitrary joint degree distribution and overlap. To illustrate
the versatility of our approach, we consider a hypothetical delayed
intervention scenario in which an immunizing agent is disseminated in a host
population to hinder the propagation of an undesirable agent (e.g. the spread
of preventive information in the context of an emerging infectious disease).Comment: Accepted for publication in Phys. Rev. E. 15 pages, 7 figure
Unconventional aspects of electronic transport in delafossite oxides
The electronic transport properties of the delafossite oxides ABO are
usually understood in terms of two well separated entities, namely, the
triangular A and (BO) layers. Here we review several cases among
this extensive family of materials where the transport depends on the
interlayer coupling and displays unconventional properties. We review the doped
thermoelectrics based on CuRhO and CuCrO, which show a high-temperature
recovery of Fermi-liquid transport exponents, as well as the highly anisotropic
metals PdCoO, PtCoO and PdCrO where the sheer simplicity of the
Fermi surface leads to unconventional transport. We present some of the
theoretical tools that have been used to investigate these transport properties
and review what can and cannot be learned from the extensive set of electronic
structure calculations that have been performed.Comment: 35 pages, 19 figure
Percolation on random networks with arbitrary k-core structure
The k-core decomposition of a network has thus far mainly served as a
powerful tool for the empirical study of complex networks. We now propose its
explicit integration in a theoretical model. We introduce a Hard-core Random
Network model that generates maximally random networks with arbitrary degree
distribution and arbitrary k-core structure. We then solve exactly the bond
percolation problem on the HRN model and produce fast and precise analytical
estimates for the corresponding real networks. Extensive comparison with
selected databases reveals that our approach performs better than existing
models, while requiring less input information.Comment: 9 pages, 5 figure
Growing networks of overlapping communities with internal structure
We introduce an intuitive model that describes both the emergence of
community structure and the evolution of the internal structure of communities
in growing social networks. The model comprises two complementary mechanisms:
One mechanism accounts for the evolution of the internal link structure of a
single community, and the second mechanism coordinates the growth of multiple
overlapping communities. The first mechanism is based on the assumption that
each node establishes links with its neighbors and introduces new nodes to the
community at different rates. We demonstrate that this simple mechanism gives
rise to an effective maximal degree within communities. This observation is
related to the anthropological theory known as Dunbar's number, i.e., the
empirical observation of a maximal number of ties which an average individual
can sustain within its social groups. The second mechanism is based on a
recently proposed generalization of preferential attachment to community
structure, appropriately called structural preferential attachment (SPA). The
combination of these two mechanisms into a single model (SPA+) allows us to
reproduce a number of the global statistics of real networks: The distribution
of community sizes, of node memberships and of degrees. The SPA+ model also
predicts (a) three qualitative regimes for the degree distribution within
overlapping communities and (b) strong correlations between the number of
communities to which a node belongs and its number of connections within each
community. We present empirical evidence that support our findings in real
complex networks.Comment: 14 pages, 8 figures, 2 table
Percolation and the effective structure of complex networks
Analytical approaches to model the structure of complex networks can be
distinguished into two groups according to whether they consider an intensive
(e.g., fixed degree sequence and random otherwise) or an extensive (e.g.,
adjacency matrix) description of the network structure. While extensive
approaches---such as the state-of-the-art Message Passing Approach---typically
yield more accurate predictions, intensive approaches provide crucial insights
on the role played by any given structural property in the outcome of dynamical
processes. Here we introduce an intensive description that yields almost
identical predictions to the ones obtained with MPA for bond percolation. Our
approach distinguishes nodes according to two simple statistics: their degree
and their position in the core-periphery organization of the network. Our
near-exact predictions highlight how accurately capturing the long-range
correlations in network structures allows to easily and effectively compress
real complex network data.Comment: 11 pages, 4 figure
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