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Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data / Yatao Zhang in Transactions in GIS, vol 26 n° 8 (December 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 8 (December 2022) . - pp 3330 - 3348 Titre : Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data Type de document : Article/Communication Auteurs : Yatao Zhang, Auteur ; Martin Raubal, Auteur Année de publication : 2022 Article en page(s) : pp 3330 - 3348 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation de Dirichlet latente [Termes IGN] appariement sémantique [Termes IGN] approche hiérarchique [Termes IGN] données multisources [Termes IGN] espace urbain [Termes IGN] flux [Termes IGN] milieu urbain [Termes IGN] point d'intérêt [Termes IGN] segmentation en régions [Termes IGN] Singapour [Termes IGN] trafic routier [Termes IGN] utilisation du sol Résumé : (auteur) Sensing urban spaces from multisource geospatial data is vital to understanding the transportation system in the urban context. However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo-semantic framework first to generate semantic representations of multi-hierarchical urban context and street-level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi-hierarchical spatial signatures of urban context and street-level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi-hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. The results of signature analysis and relationship exploration can contribute to a deeper understanding of context-aware transportation research. Numéro de notice : A2022-916 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.13005 Date de publication en ligne : 27/11/2022 En ligne : https://doi.org/10.1111/tgis.13005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102348 [article]