Trajectory analysis using data mining techniques
This study presents an innovative method for identifying variability in vehicular flow, designed for dynamic urban environments with fluctuating traffic conditions. The proposed approach integrates real-time data flow processing with a two-level clustering strategy to detect and analyze vehicular...
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| Format: | Article |
| Language: | English |
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Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2025-04-01
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| Series: | Journal of Computer Science and Technology |
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| Online Access: | https://journal.info.unlp.edu.ar/JCST/article/view/3909 |
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| _version_ | 1850173706835329024 |
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| author | Gary Reyes |
| author_facet | Gary Reyes |
| author_sort | Gary Reyes |
| collection | DOAJ |
| description |
This study presents an innovative method for identifying variability in vehicular flow, designed for dynamic
urban environments with fluctuating traffic conditions. The proposed approach integrates real-time data flow
processing with a two-level clustering strategy to detect and analyze vehicular density patterns. The first level
performs dynamic clustering of GPS locations, forming microclusters that represent spatially homogeneous
traffic zones. Each microcluster is continuously updated based on similarity criteria and a forgetting mechanism
that ensures data relevance. Periodic snapshots capture the temporal evolution of the traffic distribution, which
serves as input for the second level of clustering. The second level aggregates microclusters based on proximity,
taking advantage of historical density data to classify traffic variability. By comparing current and baseline
densities, the method identifies congestion-prone areas and dynamically adjusts cluster formations. This twolevel
approach improves traffic management and provides a robust framework for detecting congestion trends.
Through validation in three urban case studies, San Francisco, Rome and Guayaquil, the methodology
successfully captured the spatial and temporal variability of traffic, identifying congestion hotspots and
uncovering patterns of flow evolution over time.
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| format | Article |
| id | doaj-art-ef298a8181ec46029cd4fa3386d41fe7 |
| institution | OA Journals |
| issn | 1666-6046 1666-6038 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata |
| record_format | Article |
| series | Journal of Computer Science and Technology |
| spelling | doaj-art-ef298a8181ec46029cd4fa3386d41fe72025-08-20T02:19:47ZengPostgraduate Office, School of Computer Science, Universidad Nacional de La PlataJournal of Computer Science and Technology1666-60461666-60382025-04-0125110.24215/16666038.25.e06Trajectory analysis using data mining techniquesGary Reyes0Universidad de Guayaquil This study presents an innovative method for identifying variability in vehicular flow, designed for dynamic urban environments with fluctuating traffic conditions. The proposed approach integrates real-time data flow processing with a two-level clustering strategy to detect and analyze vehicular density patterns. The first level performs dynamic clustering of GPS locations, forming microclusters that represent spatially homogeneous traffic zones. Each microcluster is continuously updated based on similarity criteria and a forgetting mechanism that ensures data relevance. Periodic snapshots capture the temporal evolution of the traffic distribution, which serves as input for the second level of clustering. The second level aggregates microclusters based on proximity, taking advantage of historical density data to classify traffic variability. By comparing current and baseline densities, the method identifies congestion-prone areas and dynamically adjusts cluster formations. This twolevel approach improves traffic management and provides a robust framework for detecting congestion trends. Through validation in three urban case studies, San Francisco, Rome and Guayaquil, the methodology successfully captured the spatial and temporal variability of traffic, identifying congestion hotspots and uncovering patterns of flow evolution over time. https://journal.info.unlp.edu.ar/JCST/article/view/3909Clustering algorithm, Congestion detection, GPS trajectory, Traffic flow, Trajectory clusterin |
| spellingShingle | Gary Reyes Trajectory analysis using data mining techniques Journal of Computer Science and Technology Clustering algorithm, Congestion detection, GPS trajectory, Traffic flow, Trajectory clusterin |
| title | Trajectory analysis using data mining techniques |
| title_full | Trajectory analysis using data mining techniques |
| title_fullStr | Trajectory analysis using data mining techniques |
| title_full_unstemmed | Trajectory analysis using data mining techniques |
| title_short | Trajectory analysis using data mining techniques |
| title_sort | trajectory analysis using data mining techniques |
| topic | Clustering algorithm, Congestion detection, GPS trajectory, Traffic flow, Trajectory clusterin |
| url | https://journal.info.unlp.edu.ar/JCST/article/view/3909 |
| work_keys_str_mv | AT garyreyes trajectoryanalysisusingdataminingtechniques |