ponencia en congreso.page.titleprefix Data-driven simulation for pedestrian avoiding a fixed obstacle
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Date
2019-07
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Abstract
"Data-driven simulation of pedestrian dynamics is an incipient and promising approach for building reliable microscopic pedestrian models. We propose a methodology based on generalized regression neural networks, which does not have to deal with a huge number of free parameters as in the case of multilayer neural networks. Although the method is general, we focus on the one pedestrian—one obstacle problem. The proposed model allows us to simulate the trajectory of a pedestrian avoiding an obstacle from any direction."
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FLUJO CONFINADO, MATERIALES GRANULARES, PEATONES, REDES NEURONALES