Data Stream Processing Method for Clustering of Trajectories

Reyes, Gary; Lanzarini, Laura Cristina; Estrebou, César Armando; Bariviera, Aurelio F.

doi:10.1007/978-3-031-19961-5_11

Cited by 1 publication

(2 citation statements)

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“…There are identification proposals for static clustering that use, as a basis, a fixed grid-based technique to process data streams from various features [17,18]. Although this processing method leads to the identification of different congestion patterns in a simple way [19], they have the disadvantage that they keep in the background the temporal characteristic present in the data of the trajectories; this disadvantage causes a situation where the information of the clusters can present persistent patterns that should not be present if it is analyzed temporally.…”

Section: Related Workmentioning

confidence: 99%

“…Conversely, emphasizing the need to account for the dynamics of vehicular flow in traffic management, one viable option is to handle trajectory data in brief, periodic flows, allowing for more frequent updates to the clusters [20]; this alternative has the disadvantage that these results entail a slight delay to keep the clusters updated for each period, being unfavorable for cases where it is required to show results in real time.…”

Section: Related Workmentioning

confidence: 99%

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Method for the Identification and Classification of Zones with Vehicular Congestion

Reyes,

Tolozano-Benites,

Lanzarini

et al. 2024

IJGI

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Persistently, urban regions grapple with the ongoing challenge of vehicular traffic, a predicament fueled by the incessant expansion of the population and the rise in the number of vehicles on the roads. The recurring challenge of vehicular congestion casts a negative influence on urban mobility, thereby diminishing the overall quality of life of residents. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome, and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns, and an indicator was applied to identify and evaluate the congestion conditions of the areas. The findings indicate a heightened level of precision and recall in congestion classification when contrasted with an approach relying on static cells.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%