57,706 research outputs found
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 199
This bibliography lists 82 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979
Delays, Inaccuracies and Anticipation in Microscopic Traffic Models
We generalize a wide class of time-continuous microscopic traffic models to
include essential aspects of driver behaviour not captured by these models.
Specifically, we consider (i) finite reaction times, (ii) estimation errors,
(iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal
anticipation. The estimation errors are modelled as stochastic Wiener processes
and lead to time-correlated fluctuations of the acceleration.
We show that the destabilizing effects of reaction times and estimation
errors can essentially be compensated for by spatial and temporal anticipation,
that is, the combination of stabilizing and destabilizing effects results in
the same qualitative macroscopic dynamics as that of the respectively
underlying simple car-following model. In many cases, this justifies the use of
simplified, physics-oriented models with a few parameters only. Although the
qualitative dynamics is unchanged, multi-anticipation increase both spatial and
temporal scales of stop-and-go waves and other complex patterns of congested
traffic in agreement with real traffic data. Remarkably, the anticipation
allows accident-free smooth driving in complex traffic situations even if
reaction times exceed typical time headways.Comment: Major revision of the model and the simulations. Particularly, the
number of model parameters has been reduce
Opinion dynamics: models, extensions and external effects
Recently, social phenomena have received a lot of attention not only from
social scientists, but also from physicists, mathematicians and computer
scientists, in the emerging interdisciplinary field of complex system science.
Opinion dynamics is one of the processes studied, since opinions are the
drivers of human behaviour, and play a crucial role in many global challenges
that our complex world and societies are facing: global financial crises,
global pandemics, growth of cities, urbanisation and migration patterns, and
last but not least important, climate change and environmental sustainability
and protection. Opinion formation is a complex process affected by the
interplay of different elements, including the individual predisposition, the
influence of positive and negative peer interaction (social networks playing a
crucial role in this respect), the information each individual is exposed to,
and many others. Several models inspired from those in use in physics have been
developed to encompass many of these elements, and to allow for the
identification of the mechanisms involved in the opinion formation process and
the understanding of their role, with the practical aim of simulating opinion
formation and spreading under various conditions. These modelling schemes range
from binary simple models such as the voter model, to multi-dimensional
continuous approaches. Here, we provide a review of recent methods, focusing on
models employing both peer interaction and external information, and
emphasising the role that less studied mechanisms, such as disagreement, has in
driving the opinion dynamics. [...]Comment: 42 pages, 6 figure
Phase transitions in social impact models of opinion formation
We study phase transitions in models of opinion formation which are based on
the social impact theory. Two different models are discussed: (i) a
cellular--automata based model of a finite group with a strong leader where
persons can change their opinions but not their spatial positions, and (ii) a
model with persons treated as active Brownian particles interacting via a
communication field. In the first model, two stable phases are possible: a
cluster around the leader, and a state of social unification. The transition
into the second state occurs for a large leader strength and/or for a high
level of social noise. In the second model, we find three stable phases, which
correspond either to a ``paramagnetic'' phase (for high noise and strong
diffusion), a ``ferromagnetic'' phase (for small nose and weak diffusion), or a
phase with spatially separated ``domains'' (for intermediate conditions).Comment: 15 pages, 4 figures, submitted for publication in Physica
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