Method for the Identification and Classification of Zones with Vehicular Congestion
Bibliographic Data
| ID | 22033180 |
|---|---|
| Authors | Gary Reyes (0000-0002-3711-1906, University of Guayaquil, corresponding author), Roberto Tolozano-Benites (0000-0002-4164-5839, Universidad Andina Simón Bolívar), Laura Lanzarini (0000-0001-7027-7564, Universidad Nacional de La Plata), César Estrebou (0000-0001-5926-8827, Universidad Nacional de La Plata), Aurelio F Bariviera (0000-0003-1014-1010, Universitat Rovira i Virgili), Julio Barzola-Monteses (0000-0003-2732-979X, University of Guayaquil) |
| Year | 2024 |
| Volume | 13 |
| Issue | 3 |
| Pages | 73 |
| Publication date | 2024-02-28 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13030073 |
| OpenAlex | W4392241107 |
| Language | EN |
| References cited | 32 |
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
Biology · Automated Road and Building Extraction · Computer Science · Data Management and Algorithms · Traffic Prediction and Management Techniques
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| Citation velocity | historical |
|---|---|
| Highly cited | No |