烤架
发表于 2025-3-23 11:27:28
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 Specifically, we debate the use of outdated assumptions of crowd be
glisten
发表于 2025-3-23 16:36:12
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散布
发表于 2025-3-23 18:34:10
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
发表于 2025-3-23 22:55:16
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
发表于 2025-3-24 02:44:35
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
发表于 2025-3-24 07:11:59
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
Inferior
发表于 2025-3-24 11:19:47
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conference
发表于 2025-3-24 17:14:46
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
正式通知
发表于 2025-3-24 20:14:06
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Indigence
发表于 2025-3-25 02:44:06
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