@article{ref1,
title="Applying time-dependent attributes to represent demand in road mass transit systems",
journal="Entropy (Basel, Switzerland)",
year="2018",
author="Cristóbal, Teresa and Padrón, Gabino and Lorenzo-Navarro, Javier and Quesada-Arencibia, Alexis and García, Carmelo R.",
volume="20",
number="2",
pages="e20020133-e20020133",
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.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e20020133",
url="http://dx.doi.org/10.3390/e20020133"
}