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Andreas Schadschneider
Researcher at University of Cologne
Publications - 367
Citations - 22171
Andreas Schadschneider is an academic researcher from University of Cologne. The author has contributed to research in topics: Cellular automaton & Traffic flow. The author has an hindex of 66, co-authored 358 publications receiving 20856 citations. Previous affiliations of Andreas Schadschneider include Stony Brook University & Indian Institute of Technology Kanpur.
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Book ChapterDOI
On Force-Based Modeling of Pedestrian Dynamics
TL;DR: This work focuses on space-continuous models which include interactions between the pedestrian by forces and side-effects of spatially continuous force-based models, especially oscillations and overlapping which occur for erroneous choices of the forces.
Journal ArticleDOI
Exploring the behavior of self-organized queuing for pedestrian flow through a non-service bottleneck
TL;DR: An agent-based cellular automata that allows agents to perceive and act from the order of the social environment in real time has been presented and the simulated results show an extremely high-ordered environment is not favorable for the collective egress of human crowds as expected.
Journal ArticleDOI
Intra-cellular traffic: bio-molecular motors on filamentary tracks
Debashish Chowdhury,Aakash Basu,Ashok Garai,Philip Greulich,Katsuhiro Nishinari,Andreas Schadschneider,Tripti Tripathi +6 more
TL;DR: In this paper, the authors introduced novel quantities for characterizing the nature of the spatio-temporal organization of molecular motors on their tracks and showed how the traffic-like intracellular collective phenomena depend on the mechano-chemistry of the corresponding individual motors.
Journal ArticleDOI
A one-dimensional integrable model of fermions with multi-particle hopping
TL;DR: In this paper, a model with both single-particle and multiparticle hopping of electrons on a one-dimensional "triangular" lattice is formulated and solved exactly by Bethe ansatz.
Book ChapterDOI
Prediction of Pedestrian Speed with Artificial Neural Networks
Antoine Tordeux,Antoine Tordeux,Mohcine Chraibi,Armin Seyfried,Armin Seyfried,Andreas Schadschneider +5 more
TL;DR: In this article, the authors compare estimations of pedestrian speed with a classical model and a neural network for combinations of corridor and bottleneck experiments and show that the neural network is able to differentiate the two geometries and to improve the estimation of pedestrian speeds.