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 de...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor principal: Reyes Zambrano, Gary Xavier
Formato: Articulo Contribucion a revista
Lenguaje:Inglés
Publicado: 2025
Materias:
Ciencias Informáticas
vehicular flow
real-time data flow processing
spatial and temporal variability of traffic
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/178722
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-178722
record_format dspace
spelling I19-R120-10915-1787222025-05-30T13:15:00Z http://sedici.unlp.edu.ar/handle/10915/178722 Trajectory Analysis using Data Mining Techniques Reyes Zambrano, Gary Xavier 2025-04 2025-05-06T15:40:48Z en Ciencias Informáticas vehicular flow real-time data flow processing spatial and temporal variability of traffic 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. En "'Documentos relacionados"' se puede acceder al texto completo de la tesis. Facultad de Informática Articulo Contribucion a revista http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 68-69
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
vehicular flow
real-time data flow processing
spatial and temporal variability of traffic
spellingShingle Ciencias Informáticas
vehicular flow
real-time data flow processing
spatial and temporal variability of traffic
Reyes Zambrano, Gary Xavier
Trajectory Analysis using Data Mining Techniques
topic_facet Ciencias Informáticas
vehicular flow
real-time data flow processing
spatial and temporal variability of traffic
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.
format Articulo
Contribucion a revista
author Reyes Zambrano, Gary Xavier
author_facet Reyes Zambrano, Gary Xavier
author_sort Reyes Zambrano, Gary Xavier
title Trajectory Analysis using Data Mining Techniques
title_short 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_sort trajectory analysis using data mining techniques
publishDate 2025
url http://sedici.unlp.edu.ar/handle/10915/178722
work_keys_str_mv AT reyeszambranogaryxavier trajectoryanalysisusingdataminingtechniques
_version_ 1855354272269467648

Ejemplares similares