16,892 research outputs found
Social distancing with the Optimal Steps Model
With the Covid-19 pandemic an urgent need to simulate social distancing
arises. The Optimal Steps Model (OSM) is a pedestrian locomotion model that
operationalizes an individual's need for personal space. We present new
parameter values for personal space in the Optimal Steps Model to simulate
social distancing in the pedestrian dynamics simulator Vadere. Our approach is
pragmatic. We consider two use cases: in the first we demand that a set social
distance must never be violated. In the second the social distance must be kept
only on average. For each use case we conduct simulation studies in a typical
bottleneck scenario and measure contact times, that is, violations of the
social distance rule. We derive rules of thumb for suitable parameter choices
in dependency of the desired social distance. We test the rules of thumb for
the social distances 1.5m and 2.0m and observe that the new parameter values
indeed lead to the desired social distancing. Thus, the rules of thumb will
quickly enable Vadere users to conduct their own studies without understanding
the intricacies of the OSM implementation and without extensive parameter
adjustment.Comment: 9 pages, 8 figures, 4 table
Physics-based modeling and data representation of pedestrian pairwise interactions
The possibility to understand and to quantitatively model the physics of the
interactions between pedestrians walking in crowds has compelling relevant
applications, e.g. related to the design and safety of civil infrastructures.
In this work we study pedestrian-pedestrian interactions from observational
experimental data in diluted crowds. While in motion, pedestrians adapt their
walking paths trying to preserve mutual comfort distances and to avoid
collisions. In mathematical models this behavior is typically modeled via
"social" interaction forces.
Leveraging on a high-quality, high-statistics dataset - composed of few
millions of real-life trajectories acquired from state-of-the-art observational
experiments - we develop a quantitative model capable of addressing
interactions in the case of binary collision avoidance. We model interactions
in terms of both long- and short-range forces, which we superimpose to our
Langevin model for non-interacting pedestrian motion [Corbetta et al.
Phys.Rev.E 95, 032316, 2017]. The new model that we propose here features a
Langevin dynamics with "fast" random velocity fluctuations that are
superimposed to the "slow" dynamics of a hidden model variable: the "intended"
walking path. The model is capable of reproducing relevant statistics of the
collision avoidance motion, such as the statistics of the side displacement and
of the passing speed. Rare occurrences of bumping events are also recovered.
Furthermore, comparing with large datasets of real-life tracks involves an
additional challenge so far neglected: identifying, within a database
containing very heterogeneous conditions, only the relevant events
corresponding to binary avoidance interactions. To tackle this challenge, we
propose a novel approach based on a graph representation of pedestrian
trajectories, which allows us to operate complexity reduction for efficient
data selection.Comment: 17 figures, 18 page
Quickest Paths in Simulations of Pedestrians
This contribution proposes a method to make agents in a microscopic
simulation of pedestrian traffic walk approximately along a path of estimated
minimal remaining travel time to their destination. Usually models of
pedestrian dynamics are (implicitly) built on the assumption that pedestrians
walk along the shortest path. Model elements formulated to make pedestrians
locally avoid collisions and intrusion into personal space do not produce
motion on quickest paths. Therefore a special model element is needed, if one
wants to model and simulate pedestrians for whom travel time matters most (e.g.
travelers in a station hall who are late for a train). Here such a model
element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte
The Effect of Integrating Travel Time
This contribution demonstrates the potential gain for the quality of results
in a simulation of pedestrians when estimated remaining travel time is
considered as a determining factor for the movement of simulated pedestrians.
This is done twice: once for a force-based model and once for a cellular
automata-based model. The results show that for the (degree of realism of)
simulation results it is more relevant if estimated remaining travel time is
considered or not than which modeling technique is chosen -- here force-based
vs. cellular automata -- which normally is considered to be the most basic
choice of modeling approach.Comment: preprint of Pedestrian and Evacuation 2012 conference (PED2012)
contributio
GPU accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian Movement
Pedestrian movement, although ubiquitous and well-studied, is still not that
well understood due to the complicating nature of the embedded social dynamics.
Interest among researchers in simulating pedestrian movement and interactions
has grown significantly in part due to increased computational and
visualization capabilities afforded by high power computing. Different
approaches have been adopted to simulate pedestrian movement under various
circumstances and interactions. In the present work, bi-directional crowd
movement is simulated where an equal numbers of individuals try to reach the
opposite sides of an environment. Two movement methods are considered. First a
Least Effort Model (LEM) is investigated where agents try to take an optimal
path with as minimal changes from their intended path as possible. Following
this, a modified form of Ant Colony Optimization (ACO) is proposed, where
individuals are guided by a goal of reaching the other side in a least effort
mode as well as a pheromone trail left by predecessors. The basic idea is to
increase agent interaction, thereby more closely reflecting a real world
scenario. The methodology utilizes Graphics Processing Units (GPUs) for general
purpose computing using the CUDA platform. Because of the inherent parallel
properties associated with pedestrian movement such as proximate interactions
of individuals on a 2D grid, GPUs are well suited. The main feature of the
implementation undertaken here is that the parallelism is data driven. The data
driven implementation leads to a speedup up to 18x compared to its sequential
counterpart running on a single threaded CPU. The numbers of pedestrians
considered in the model ranged from 2K to 100K representing numbers typical of
mass gathering events. A detailed discussion addresses implementation
challenges faced and averted
The Inflection Point of the Speed-Density Relation and the Social Force Model
It has been argued that the speed-density digram of pedestrian movement has
an inflection point. This inflection point was found empirically in
investigations of closed-loop single-file pedestrian movement. The reduced
complexity of single-file movement does not only allow a higher precision for
the evaluation of empirical data, but it occasionally also allows analytical
considerations for micosimulation models. In this way it will be shown that
certain (common) variants of the Social Force Model (SFM) do not produce an
inflection point in the speed-density diagram if infinitely many pedestrians
contribute to the force computed for one pedestrian. We propose a modified
Social Force Model that produces the inflection point.Comment: accepted for presentation at conference Traffic and Granular Flow
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Pedestrian vision and collision avoidance behavior: investigation of the information process space of pedestrians using an eye tracker
This study investigates the Information Process Space (IPS) of
pedestrians, which has been widely used in microscopic pedestrian
movement simulation models. IPS is a conceptual framework to define the
spatial extent within which all objects are considered as potential obstacles
for each pedestrian when computing where to move next. The particular
focus of our study was identifying the size and shape of IPS by examining
observed gaze patterns of pedestrians. A series of experiments was conducted
in a controlled laboratory environment, in which up to 4 participants walked
on a platform at their natural speed. Their gaze patterns were recorded by a
head-mounted eye tracker and walking paths by laser-range-scanner–based
tracking systems at the frequency of 25Hz. Our findings are threefold:
pedestrians pay much more attention to ground surfaces to detect immediate
potential environmental hazards than fixating on obstacles; most of their
fixations fall within a cone-shape area rather than a semicircle; and the
attention paid to approaching pedestrians is not as high as that paid to static
obstacles. These results led to an insight that the structure of IPS should be
re-examined by researching directional characteristics of pedestrians’ vision
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