Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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.

30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom