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

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 reg...

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Autor Principal: Teknomo, Kardi
Otros Autores: Estuar, Ma. Regina
Formato: info:eu-repo/semantics/article
Idioma: spa
Publicado: Editorial Bonaventuriana 2018
Materias:
Acceso en línea: Teknomo, K., & Estuar, M. R. (2010). Clustering as an EDA method: the case of pedestrian directional flow behavior. International Journal of Psychological Research, 3(1), 23–36. https://doi.org/10.21500/20112084.820
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Sumario: 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.