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http://hdl.handle.net/10553/106206
Título: | Data framework for road-based mass transit systems data mining project | Autores/as: | Cristóbal, Teresa Padrón, Gabino Quesada-Arencibia, Alexis Alayón, Francisco García, Carmelo R. |
Clasificación UNESCO: | 120304 Inteligencia artificial 3327 Tecnología de los sistemas de transporte |
Palabras clave: | Intelligent transport systems Data mining Mass transit systems |
Fecha de publicación: | 2019 | Publicación seriada: | Proceedings (MDPI) | Conferencia: | 13th International Conference on Ubiquitous Computing and Ambient Intelligence - UCAmI 2019 | Resumen: | The current paradigm of intelligent transport systems (ITS) is based on the continuous observation of what is happening in the transport network and the continuous processing of data coming from these observations. This implies the handling and processing of a massive amount of data, and for this reason, data mining and big data are fields increasingly used in transportation engineering. A framework to facilitate the phases of data preparation and knowledge modeling in the context of data mining projects for road-based mass transit systems is presented in this paper. To illustrate the utility of the framework, its utilization in the analysis of travel time in a road-based mass transit system is presented as a use case. | URI: | http://hdl.handle.net/10553/106206 | ISSN: | 2504-3900 | DOI: | 10.3390/proceedings2019031025 | Fuente: | Proceedings (MDPI) [ISSN 2504-3900], v. 31 (1), 25 (2019) |
Colección: | Artículos |
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