Road data enrichment framework based on heterogeneous data fusion for ITS.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorRettore, Paulo Henrique Lopes-
Autor(es): dc.creatorSantos, Bruno Pereira dos-
Autor(es): dc.creatorLopes, Roberto Rigolin Ferreira-
Autor(es): dc.creatorMenezes, João Guilherme Maia de-
Autor(es): dc.creatorVillas, Leandro Aparecido-
Autor(es): dc.creatorLoureiro, Antonio Alfredo Ferreira-
Data de aceite: dc.date.accessioned2025-08-21T15:52:59Z-
Data de disponibilização: dc.date.available2025-08-21T15:52:59Z-
Data de envio: dc.date.issued2022-09-21-
Data de envio: dc.date.issued2022-09-21-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/jspui/handle/123456789/15457-
Fonte completa do material: dc.identifierhttps://doi.org/10.1109/TITS.2020.2971111-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1026468-
Descrição: dc.descriptionIn this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to improve the description of traffic conditions through Location- Based Social Media (LBSM) data. As a case study, we explain how T-MAPS is able to enhance routing and trajectory descriptions by using tweets. Our experiments compare T-MAPS’ routes against Google Maps’ routes, showing up to 62% of route similarity, even though T-MAPS uses fewer and coarse-grained data. We then propose three applications, Route Sentiment (RS), Route Infor- mation (RI), and Area Tags (AT), to enrich T-MAPS’ suggested routes. For the second service, we present the Twitter Incident (T-Incident), a low-cost learning-based road incident detection and enrichment approach built using heterogeneous data fusion. Our approach uses a learning-based model to identify patterns on social media data which is then used to describe a class of events, aiming to detect different types of events. Our model to detect events achieved scores above 90%, thus allowing incident detection and description as a RoDE application. As a result, the enriched event description allows ITS to better understand the LBSM user’s viewpoint about traffic events (e.g., jams) and points of interest (e.g., restaurants, theaters, stadiums).-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsaberto-
Direitos: dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. Fonte: o PDF do artigo.-
Palavras-chave: dc.subjectIncident detection-
Título: dc.titleRoad data enrichment framework based on heterogeneous data fusion for ITS.-
Aparece nas coleções:Repositório Institucional - UFOP

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