Data-driven simulation for pedestrian avoiding a fixed obstacle

"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 o...

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Autores principales: Martin, Rafael F., Parisi, Daniel
Formato: Ponencias en Congresos acceptedVersion
Lenguaje:Inglés
Publicado: 2022
Materias:
FLUJO CONFINADO
MATERIALES GRANULARES
PEATONES
REDES NEURONALES
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/3827
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-3827
record_format dspace
spelling I32-R138-123456789-38272022-12-07T14:13:33Z Data-driven simulation for pedestrian avoiding a fixed obstacle Martin, Rafael F. Parisi, Daniel FLUJO CONFINADO MATERIALES GRANULARES PEATONES REDES NEURONALES "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." 2022-04-28T16:10:35Z 2022-04-28T16:10:35Z 2019-07 Ponencias en Congresos info:eu-repo/semantics/acceptedVersion 0930-8989 http://ri.itba.edu.ar/handle/123456789/3827 en info:eu-repo/grantAgreement/ANPCyT/PID/2015-003/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/ITBACyT/2018-42/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-55973-1_25 application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic FLUJO CONFINADO
MATERIALES GRANULARES
PEATONES
REDES NEURONALES
spellingShingle FLUJO CONFINADO
MATERIALES GRANULARES
PEATONES
REDES NEURONALES
Martin, Rafael F.
Parisi, Daniel
Data-driven simulation for pedestrian avoiding a fixed obstacle
topic_facet FLUJO CONFINADO
MATERIALES GRANULARES
PEATONES
REDES NEURONALES
description "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."
format Ponencias en Congresos
acceptedVersion
author Martin, Rafael F.
Parisi, Daniel
author_facet Martin, Rafael F.
Parisi, Daniel
author_sort Martin, Rafael F.
title Data-driven simulation for pedestrian avoiding a fixed obstacle
title_short Data-driven simulation for pedestrian avoiding a fixed obstacle
title_full Data-driven simulation for pedestrian avoiding a fixed obstacle
title_fullStr Data-driven simulation for pedestrian avoiding a fixed obstacle
title_full_unstemmed Data-driven simulation for pedestrian avoiding a fixed obstacle
title_sort data-driven simulation for pedestrian avoiding a fixed obstacle
publishDate 2022
url http://ri.itba.edu.ar/handle/123456789/3827
work_keys_str_mv AT martinrafaelf datadrivensimulationforpedestrianavoidingafixedobstacle
AT parisidaniel datadrivensimulationforpedestrianavoidingafixedobstacle
_version_ 1765660998106087424

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