Title: Applying time-dependent attributes to represent demand in road mass transit systems
Authors: Cristóbal, Teresa
Padrón, Gabino 
Lorenzo-Navarro, Javier 
Quesada-Arencibia, Alexis 
García, Carmelo R. 
UNESCO Clasification: 120304 Inteligencia artificial
3327 Tecnología de los sistemas de transporte
Keywords: Clustering
Entropy
Attribute creation
Data mining
Intelligent transport systems
Mass transit systems
Demand
Issue Date: 2018
Journal: Entropy 
Abstract: The development of efficient mass transit systems that provide quality of service is a major challenge for modern societies. To meet this challenge, it is essential to understand user demand. This article proposes using new time-dependent attributes to represent demand, attributes that differ from those that have traditionally been used in the design and planning of this type of transit system. Data mining was used to obtain these new attributes; they were created using clustering techniques, and their quality evaluated with the Shannon entropy function and with neural networks. The methodology was implemented on an intercity public transport company and the results demonstrate that the attributes obtained offer a more precise understanding of demand and enable predictions to be made with acceptable precision.
URI: http://hdl.handle.net/10553/41822
ISSN: 1099-4300
DOI: 10.3390/e20020133
Source: Entropy [ISSN 1099-4300], v. 20 (2), 133
Appears in Collections:Artículos

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