Identificador persistente para citar o vincular este elemento: 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
miniatura
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