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Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics
By applying spatio-temporal aggregation to traffic data consisting of vehicle trajectories, we generate a spatially abstracted transportation network, which is a directed graph where nodes stand for territory compartments (areas in geographic space) and links (edges) are abstractions of the possible paths between neighboring areas. From time series of traffic characteristics obtained for the links, we reconstruct mathematical models of the interdependencies between the traffic intensity (a.k.a. traffic flow or flux) and mean velocity. Graphical representations of these interdependencies have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We present the process of data-driven generation of traffic forecasting and simulation models, in which each step is supported by visual analytics techniques
APPROXIMATE STOCHASTIC TECHNIQUES FOR DIVERSE ENGINEERING DYNAMICS APPLICATIONS
Generally, deterministic approaches are used in practice to analyze dynamic systems. Variations in loading conditions and material properties are taken into account by either selecting high, low or average values. Consequently, the uncertainty inherent in almost every dynamic analysis is considered just intuitively. To realistically capture the behavior of a dynamic system the intrinsic randomness must be appropriately modeled requiring concepts and methods of mathematical statistics and probability theory, as well as, random vibration theory. Undeniably, stochastic dynamics based approaches provide a more realistic modeling of the dynamic response of engineered systems allowing for enhanced design solutions. The prevailing approach used in the industry is the Monte Carlo simulation method. However, a well-known shortcoming of the method is the extensive computational cost required. Further, the class of problems of nonlinear random vibrations that lend themselves to exact solutions (e.g., via the associated Fokker-Planck-Kolmogorov equation) is extremely limited. Therefore, approximate approaches are desired for solving nonlinear stochastic dynamics problems. The current thesis seeks to exploit approximate stochastic dynamics tools to solve engineering dynamics problems encountered in practice. In particular, the primary focus is directed towards the recently developed Wiener path integral technique, which has been shown to poses certain advantages over alternative well-established solution methodologies, namely, computational efficiency and accuracy. Two applications are investigated: the stochastic response of nonlinear vibratory energy harvesters, and, the depth determination of ice gouging events. The accuracy/reliability of the approximate approaches is demonstrated via comparisons with pertinent Monte Carlo simulation data
New Approaches To Photometric Redshift Prediction Via Gaussian Process Regression In The Sloan Digital Sky Survey
Expanding upon the work of Way and Srivastava 2006 we demonstrate how the use
of training sets of comparable size continue to make Gaussian process
regression (GPR) a competitive approach to that of neural networks and other
least-squares fitting methods. This is possible via new large size matrix
inversion techniques developed for Gaussian processes (GPs) that do not require
that the kernel matrix be sparse. This development, combined with a
neural-network kernel function appears to give superior results for this
problem. Our best fit results for the Sloan Digital Sky Survey (SDSS) Main
Galaxy Sample using u,g,r,i,z filters gives an rms error of 0.0201 while our
results for the same filters in the luminous red galaxy sample yield 0.0220. We
also demonstrate that there appears to be a minimum number of training-set
galaxies needed to obtain the optimal fit when using our GPR rank-reduction
methods. We find that morphological information included with many photometric
surveys appears, for the most part, to make the photometric redshift evaluation
slightly worse rather than better. This would indicate that most morphological
information simply adds noise from the GP point of view in the data used
herein. In addition, we show that cross-match catalog results involving
combinations of the Two Micron All Sky Survey, SDSS, and Galaxy Evolution
Explorer have to be evaluated in the context of the resulting cross-match
magnitude and redshift distribution. Otherwise one may be misled into overly
optimistic conclusions.Comment: 32 pages, ApJ in Press, 2 new figures, 1 new table of comparison
methods, updated discussion, references and typos to reflect version in Pres
Solar cycle effects in planetary geomagnetic activity: Analysis of 36âyear long OMNI dataset
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94796/1/grl13462.pd
A Gas Leak Rate Measurement System for the ATLAS MUON BIS-Monitored Drift Tubes
A low-cost, reliable and precise system developed for the gas leak rate measurement of the BIS-Monitored Drift Tubes (MDTs) for the ATLAS Muon Spectrometer is presented. In order to meet the BIS-MDT mass production rate, a total number of 100 tubes are tested simultaneously in this setup. The pressure drop of each one of the MDT is measured, within a typical time interval of 48 hours, via a differential manometer comparing with the pressure of a gas tight reference tube. The precision of the method implemented is based on the system temperature homogeneity, with accuracy of ĂT = 0.3 oC. For this reason, two thermally isolated boxes are used testing 50 tubes each of them, to achieve high degree of temperature uniformity and stability. After measuring several thousands of the MDTs, the developed system is confirmed to be appropriate within the specifications for testing the MDTs during the mass production
Macroscopic traffic models from microscopic car-following models
We present a method to derive macroscopic fluid-dynamic models from
microscopic car-following models via a coarse-graining procedure. The method is
first demonstrated for the optimal velocity model. The derived macroscopic
model consists of a conservation equation and a momentum equation, and the
latter contains a relaxation term, an anticipation term, and a diffusion term.
Properties of the resulting macroscopic model are compared with those of the
optimal velocity model through numerical simulations, and reasonable agreement
is found although there are deviations in the quantitative level. The
derivation is also extended to general car-following models.Comment: 12 pages, 4 figures; to appear in Phys. Rev.
Generalized Force Model of Traffic Dynamics
Floating car data of car-following behavior in cities were compared to
existing microsimulation models, after their parameters had been calibrated to
the experimental data. With these parameter values, additional simulations have
been carried out, e.g. of a moving car which approaches a stopped car. It
turned out that, in order to manage such kinds of situations without producing
accidents, improved traffic models are needed. Good results have been obtained
with the proposed generalized force model.Comment: For related work see
http://www.theo2.physik.uni-stuttgart.de/helbing.htm
The closest elastic tensor of arbitrary symmetry to an elasticity tensor of lower symmetry
The closest tensors of higher symmetry classes are derived in explicit form
for a given elasticity tensor of arbitrary symmetry. The mathematical problem
is to minimize the elastic length or distance between the given tensor and the
closest elasticity tensor of the specified symmetry. Solutions are presented
for three distance functions, with particular attention to the Riemannian and
log-Euclidean distances. These yield solutions that are invariant under
inversion, i.e., the same whether elastic stiffness or compliance are
considered. The Frobenius distance function, which corresponds to common
notions of Euclidean length, is not invariant although it is simple to apply
using projection operators. A complete description of the Euclidean projection
method is presented. The three metrics are considered at a level of detail far
greater than heretofore, as we develop the general framework to best fit a
given set of moduli onto higher elastic symmetries. The procedures for finding
the closest elasticity tensor are illustrated by application to a set of 21
moduli with no underlying symmetry.Comment: 48 pages, 1 figur
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