Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/106206
Title: Data framework for road-based mass transit systems data mining project
Authors: Cristóbal, Teresa
Padrón, Gabino 
Quesada-Arencibia, Alexis 
Alayón, Francisco 
García, Carmelo R. 
UNESCO Clasification: 120304 Inteligencia artificial
3327 Tecnología de los sistemas de transporte
Keywords: Intelligent transport systems
Data mining
Mass transit systems
Issue Date: 2019
Journal: Proceedings (MDPI) 
Conference: 13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence - UCAmI 2019 
Abstract: 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
Source: Proceedings (MDPI) [ISSN 2504-3900], v. 31 (1), 25 (2019)
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