<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462024000100257</article-id>
<article-id pub-id-type="doi">10.13053/cys-28-1-4891</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Classifying Roads with Multi-Step Graph Embeddings]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Molefe]]></surname>
<given-names><![CDATA[Mohale E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tapamo]]></surname>
<given-names><![CDATA[Jules R.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,University of KwaZulu Natal School of Engineering ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>South Africa</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2024</year>
</pub-date>
<volume>28</volume>
<numero>1</numero>
<fpage>257</fpage>
<lpage>270</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462024000100257&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462024000100257&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462024000100257&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Machine learning-based road-type classification is pivotal in intelligent road network systems, where accurate network modelling is crucial. Graph embedding methods have emerged as the leading paradigm for capturing the intricate relationships within road networks. However, their effectiveness hinges on the quality of input features. This paper introduces a novel two-stage graph embedding approach used to classify road-type. The first stage employs Deep Autoencoders to produce compact representation of road segments. This compactified representation is then used, in the second stage, by graph embedding methods to generate an embedded vectors, leveraging the features of neighbouring segments. Results achieved, with experiments on realistic city road network datasets, show that the proposed method outperforms existing approaches with respect to classification accuracy.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Road type classification]]></kwd>
<kwd lng="en"><![CDATA[road networks intelligent systems]]></kwd>
<kwd lng="en"><![CDATA[graph embedding methods]]></kwd>
<kwd lng="en"><![CDATA[deep autoencoder]]></kwd>
</kwd-group>
</article-meta>
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