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

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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Bibliographic Details
Main Authors: Estuar, Ma. Regina Justina E, Teknomo, Kardi
Format: text
Published: Archīum Ateneo 2010
Subjects:
Gaussian Mixture
directional flow pattern
pedestrian behavior
trajectory analysis
Computer Sciences
Online Access:https://archium.ateneo.edu/discs-faculty-pubs/197
https://revistas.usb.edu.co/index.php/IJPR/article/view/820
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Institution: Ateneo De Manila University
Description
Summary: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.

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