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...
- Autores:
-
Teknomo, Kardi
E. Estuar, Ma. Regina
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2010
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/25699
- Acceso en línea:
- https://hdl.handle.net/10819/25699
https://doi.org/10.21500/20112084.820
- Palabra clave:
- Gaussian Mixture
directional flow pattern
pedestrian behavior
trajectory analysis
- Rights
- openAccess
- License
- International Journal of Psychological Research - 2010
| 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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