Ponencia en Congreso:
Data-driven simulation for pedestrian avoiding a fixed obstacle

dc.contributor.authorMartin, Rafael F.
dc.contributor.authorParisi, Daniel
dc.date.accessioned2022-04-28T16:10:35Z
dc.date.available2022-04-28T16:10:35Z
dc.date.issued2019-07
dc.description.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."en
dc.identifier.issn0930-8989
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/3827
dc.language.isoenen
dc.relationinfo:eu-repo/grantAgreement/ANPCyT/PID/2015-003/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/grantAgreement/ITBACyT/2018-42/AR. Ciudad Autónoma de Buenos Aires
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-55973-1_25
dc.subjectFLUJO CONFINADOes
dc.subjectMATERIALES GRANULARESes
dc.subjectPEATONESes
dc.subjectREDES NEURONALESes
dc.titleData-driven simulation for pedestrian avoiding a fixed obstacleen
dc.typePonencias en Congresoses
dc.typeinfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePonencia en Congreso
itba.description.filiationFil: Martin, Rafael F. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiationFil: Parisi, Daniel. Instituto Tecnológico de Buenos Aires; Argentina.
itba.description.filiationFil: Parisi, Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.
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