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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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | EN ES |
| Publié: |
Universidad de San Buenaventura
2010
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| Accès en ligne: | https://doaj.org/article/da0196e4f7154d3b9d4d573eb04d3ab5 |
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| Résumé: | 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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