Clustering as an EDA method: the case of pedestrian directional flow behavior.

Clustering as an EDA method: the case of pedestrian directional flow behavior.

Guardado en:

International Journal of Psychological Research

2011-2084

2011-7922

Teknomo, Kardi

E. Estuar, Ma. Regina

UNIVERSIDAD DE SAN BUENAVENTURA

10.21500/20112084.820

3

2010-06-30

23

36

Colombia

International Journal of Psychological Research - 2010

info:eu-repo/semantics/openAccess

http://purl.org/coar/access_right/c_abf2

id 3546ba0696cb55c6caf208de2986df42
record_format ojs
spelling Clustering as an EDA method: the case of pedestrian directional flow behavior.
Clustering as an EDA method: the case of pedestrian directional flow behavior.
Artículo de revista
Bierlaire, M., Antonini, G., & Weber, M. (2003). Behavioral dynamics for pedestrians. In K. Axhausen, Moving through nets: The physical and social dimensions of travel. Elsevier. Brillinger, D., Preisler, H., Haiganoush, K., Ager, A., & Kie, J. (2004). An exploratory data analysis (EDA) of the paths of moving animals. Journal of Statistical Planning and Inference 122 , 43-63. Chebat, J., Gélinas-Chebat, C., & Therrien, K. (2005). Lost in a mall, the effects of gender, familiarity with the shopping mall and the shopping values on shoppers’ way finding processes. Journal of Business Research , 58 (11), 1590– 1598. de Mast, J., & Trip, A. (2008). Exploratory data analysis in quality improvement projects. Journal of Quality Technology , 39 (4), 301-311. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B , 39 (1), 1-38.
https://revistas.usb.edu.co/index.php/IJPR/article/view/820
Inglés
https://creativecommons.org/licenses/by-nc-sa/4.0/
International Journal of Psychological Research - 2010
info:eu-repo/semantics/article
Universidad San Buenaventura - USB (Colombia)
http://purl.org/coar/resource_type/c_6501
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/version/c_970fb48d4fbd8a85
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
Text
International Journal of Psychological Research
Publication
application/pdf
3
Given the data of pedestrian trajectories in NTXY format, three clustering methods of K Means, Expectation Maximization (EM) and Affinity Propagation were utilized as Exploratory Data Analysis to find the pattern of pedestrian directional flow behavior. The analysis begins without a prior notion regarding the structure of the pattern and it consequentially infers the structure of directional flow pattern. Significant similarities in patterns for both individual and instantaneous walking angles based on EDA method are reported and explained in case studies.
Teknomo, Kardi
E. Estuar, Ma. Regina
Gaussian Mixture
directional flow pattern
Journal article
Núm. 1 , Año 2010 : Special Issue of Statistics in Psychology
1
pedestrian behavior
trajectory analysis
36
https://revistas.usb.edu.co/index.php/IJPR/article/download/820/596
2010-06-30
23
https://doi.org/10.21500/20112084.820
10.21500/20112084.820
2010-06-30T00:00:00Z
2011-7922
2010-06-30T00:00:00Z
2011-2084
institution UNIVERSIDAD DE SAN BUENAVENTURA
thumbnail https://nuevo.metarevistas.org/UNIVERSIDADDESANBUENAVENTURA_COLOMBIA/logo.png
country_str Colombia
collection International Journal of Psychological Research
title Clustering as an EDA method: the case of pedestrian directional flow behavior.
spellingShingle Clustering as an EDA method: the case of pedestrian directional flow behavior.
Teknomo, Kardi
E. Estuar, Ma. Regina
Gaussian Mixture
directional flow pattern
pedestrian behavior
trajectory analysis
title_short Clustering as an EDA method: the case of pedestrian directional flow behavior.
title_full Clustering as an EDA method: the case of pedestrian directional flow behavior.
title_fullStr Clustering as an EDA method: the case of pedestrian directional flow behavior.
title_full_unstemmed Clustering as an EDA method: the case of pedestrian directional flow behavior.
title_sort clustering as an eda method: the case of pedestrian directional flow behavior.
description_eng Given the data of pedestrian trajectories in NTXY format, three clustering methods of K Means, Expectation Maximization (EM) and Affinity Propagation were utilized as Exploratory Data Analysis to find the pattern of pedestrian directional flow behavior. The analysis begins without a prior notion regarding the structure of the pattern and it consequentially infers the structure of directional flow pattern. Significant similarities in patterns for both individual and instantaneous walking angles based on EDA method are reported and explained in case studies.
author Teknomo, Kardi
E. Estuar, Ma. Regina
author_facet Teknomo, Kardi
E. Estuar, Ma. Regina
topic Gaussian Mixture
directional flow pattern
pedestrian behavior
trajectory analysis
topic_facet Gaussian Mixture
directional flow pattern
pedestrian behavior
trajectory analysis
citationvolume 3
citationissue 1
citationedition Núm. 1 , Año 2010 : Special Issue of Statistics in Psychology
publisher Universidad San Buenaventura - USB (Colombia)
ispartofjournal International Journal of Psychological Research
source https://revistas.usb.edu.co/index.php/IJPR/article/view/820
language Inglés
format Article
rights https://creativecommons.org/licenses/by-nc-sa/4.0/
International Journal of Psychological Research - 2010
info:eu-repo/semantics/openAccess
http://purl.org/coar/access_right/c_abf2
references_eng Bierlaire, M., Antonini, G., & Weber, M. (2003). Behavioral dynamics for pedestrians. In K. Axhausen, Moving through nets: The physical and social dimensions of travel. Elsevier. Brillinger, D., Preisler, H., Haiganoush, K., Ager, A., & Kie, J. (2004). An exploratory data analysis (EDA) of the paths of moving animals. Journal of Statistical Planning and Inference 122 , 43-63. Chebat, J., Gélinas-Chebat, C., & Therrien, K. (2005). Lost in a mall, the effects of gender, familiarity with the shopping mall and the shopping values on shoppers’ way finding processes. Journal of Business Research , 58 (11), 1590– 1598. de Mast, J., & Trip, A. (2008). Exploratory data analysis in quality improvement projects. Journal of Quality Technology , 39 (4), 301-311. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B , 39 (1), 1-38.
type_driver info:eu-repo/semantics/article
type_coar http://purl.org/coar/resource_type/c_6501
type_version info:eu-repo/semantics/publishedVersion
type_coarversion http://purl.org/coar/version/c_970fb48d4fbd8a85
type_content Text
publishDate 2010-06-30
date_accessioned 2010-06-30T00:00:00Z
date_available 2010-06-30T00:00:00Z
url https://revistas.usb.edu.co/index.php/IJPR/article/view/820
url_doi https://doi.org/10.21500/20112084.820
issn 2011-2084
eissn 2011-7922
doi 10.21500/20112084.820
citationstartpage 23
citationendpage 36
url2_str_mv https://revistas.usb.edu.co/index.php/IJPR/article/download/820/596
_version_ 1797920202816487424

Código QR

Estadísticas y Métricas Alternativas

Títulos similares