Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Method for the Identification and Classification of Zones with Vehicular Congestion

Gary Reyes
* ,
Roberto Tolozano-Benites
,
Laura Lanzarini
ORCID logo ,
César Estrebou
,
Aurelio F. Bariviera
ORCID logo ,
Julio Barzola-Monteses
Version 1 : Received: 8 January 2024 / Approved: 8 January 2024 / Online: 9 January 2024 (11:45:56 CET)

A peer-reviewed article of this Preprint also exists.

Reyes, G.; Tolozano-Benites, R.; Lanzarini, L.; Estrebou, C.; Bariviera, A.F.; Barzola-Monteses, J. Method for the Identification and Classification of Zones with Vehicular Congestion. ISPRS Int. J. Geo-Inf. 2024, 13, 73. Reyes, G.; Tolozano-Benites, R.; Lanzarini, L.; Estrebou, C.; Bariviera, A.F.; Barzola-Monteses, J. Method for the Identification and Classification of Zones with Vehicular Congestion. ISPRS Int. J. Geo-Inf. 2024, 13, 73.

Abstract

Vehicular traffic in urban areas continues to be a constant challenge today, due to the continuous population growth and the increase of vehicles in circulation. Vehicular congestion, as a recurrent problem, generates a negative impact on urban mobility and citizens’ quality of life. 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 results show a high precision and recall in congestion classification compared to a method based on static cells.

Keywords

congestion; dynamic clustering; classification; GPS trajectories; road networks

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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