Study of machine learning techniques for pedestrian dynamics simulation models

Reyes-Norambuena, Pedro J.; Bekios-Calfa, Juan; Martinez Torres, Javier; Torres, Javier Martinez; IEEE

Abstract

Pedestrian modeling has made significant progress, studying the pedestrian as an individual and also their behavior in the face of obstacles in their environment, from confined access spaces to the interaction of the movement of a large number of other people. In this paper we wish to explore hybrid models that allow simulating pedestrian dynamics supported by machine learning techniques to make. The results of previous work, together with the experiments of this work, are favorable for advancing toward a methodology that can incorporate the development of pedestrian dynamics simulation based on machine learning models.

Más información

Título según WOS: ID WOS:001037288000026 Not found in local WOS DB
Título según SCOPUS: ID SCOPUS_ID:85166619947 Not found in local SCOPUS DB
Fecha de publicación: 2023
DOI:

10.1109/ICPRS58416.2023.10179045

Notas: ISI, SCOPUS - SCOPUS