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Entropy 2018, 20(2), 133; https://doi.org/10.3390/e20020133

Applying Time-Dependent Attributes to Represent Demand in Road Mass Transit Systems

1
Institute for Cybernetics, Campus de Tafira, Las Palmas de Gran Canaria, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain
2
University Institute of Intelligent Systems and Numeric Applications in Engineering, Campus de Tafira, Las Palmas de Gran Canaria, University of Las Palmas de Gran Canaria, 35017 Las Palmas, Spain
*
Author to whom correspondence should be addressed.
Received: 24 January 2018 / Revised: 15 February 2018 / Accepted: 16 February 2018 / Published: 20 February 2018
(This article belongs to the Special Issue Entropy-based Data Mining)
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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. View Full-Text
Keywords: clustering; entropy; attribute creation; data mining; intelligent transport systems; mass transit systems; demand clustering; entropy; attribute creation; data mining; intelligent transport systems; mass transit systems; demand
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Cristóbal, T.; Padrón, G.; Lorenzo-Navarro, J.; Quesada-Arencibia, A.; García, C.R. Applying Time-Dependent Attributes to Represent Demand in Road Mass Transit Systems. Entropy 2018, 20, 133.

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