Titlebook: Crowd Dynamics, Volume 2; Theory, Models, and Livio Gibelli Book 2020 Springer Nature Switzerland AG 2020 Crowd Dynamics.Crowd Dynamics Bo

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https://doi.org/10.1007/978-94-011-3694-5is chapter, we provide an overview of theoretical insights from crowd psychology on intragroup and intergroup behaviour and discuss possible avenues for implementing principles of the social identity approach into pedestrian models. Specifically, we debate the use of outdated assumptions of crowd be

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散布

Philip A. Luelsdorff,E. Ann Eyland circumstances, and for the design of control strategies for different purposes. In this paper, some mathematical contribution in this field are reviewed that include different modeling issues concerning the control of a single pedestrian subject to perturbation and the development of a framework fo

Noisome

Hyperlexia: Definition and Criterion,n field limits. Some of the aspects are highlighted in a detailed discussion of a particular controlled particle dynamics. The applied techniques are shown on this simple problem to illustrate the basic methods. Computational results confirming the theoretical findings are presented and further part

GIST

Philip A. Luelsdorff,E. Ann Eylandians. We focus on two control strategies: the first one consists in using special agents, called ., to steer the crowd towards the desired direction. Leaders can be either hidden in the crowd or recognizable as such. This strategy heavily relies on the power of the . (herding effect), namely the nat

calamity

Philip A. Luelsdorff,E. Ann Eylandt can be used to support road management, signal control, and public transit. More specifically, a layered road structure, originally designed for car traffic simulations, was extended to interact with a one-dimensional tram model and one-/two-dimensional pedestrian models. The newly implemented ped

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conference

Artificial Neural Networks for the Estimation of Pedestrian Interaction Forces,al approximation for the unknown interaction forces between pedestrians. We train the artificial neural network simultaneously with other parameters arising in the model by utilizing a tailored cost function and stochastic gradient techniques. We test our approach using real data sets for the unidir

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