<?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>2007-3607</journal-id>
<journal-title><![CDATA[PAAKAT: revista de tecnología y sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[PAAKAT: rev. tecnol. soc.]]></abbrev-journal-title>
<issn>2007-3607</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Guadalajara, Sistema de Universidad Virtual]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-36072025000100002</article-id>
<article-id pub-id-type="doi">10.32870/pk.a15n28.895</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estimación de un índice de tráfico urbano mediante datos del servicio de Google Directions y su correlación con variables demográficas]]></article-title>
<article-title xml:lang="en"><![CDATA[Estimation of an urban traffic index based on data from the Google Directions service and its correlation with demographic variables]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nevárez Rodríguez]]></surname>
<given-names><![CDATA[Myrna Concepción]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vazquez Quintero]]></surname>
<given-names><![CDATA[Griselda]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García González]]></surname>
<given-names><![CDATA[Cesar Guillero]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Chihuahua  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>15</volume>
<numero>28</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-36072025000100002&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-36072025000100002&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-36072025000100002&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las ciudades crecen continuamente, causando que la capacidad de infraestructura vial sea rebasada durante algunas horas del día, siendo el monitoreo de tráfico de gran importancia. Sin embargo, no existe un método de monitoreo municipal eficiente cercano a tiempo real que muestre las problemáticas viales para la adecuada planeación urbana, por lo cual esta investigación propone el uso de un índice de congestión de tráfico urbano, usando datos gubernamentales abiertos y del servicio de Google Directions, así como su correlación con variables demográficas. La metodología propuesta mostró resultados repetibles para el índice de congestión de tráfico (p&gt;0.7), pudiendo diferenciar la congestión de tráfico temporal y espacialmente (p&lt;0.05). Se encontraron correlaciones significativas entre el índice de congestión promedio, la densidad poblacional, así como con la densidad vehicular (r=0.97 y r=0.91, respectivamente). Se confirmó que la marginación es un factor significativo (p&lt;0.05) en el índice de tráfico, el tiempo de trayecto y la distancia recorrida.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The cities are continuously growing, causing their traffic exceed their road infrastructure capacities during several hours of the day, and the traffic congestion monitoring is taking great relevance. However, there is not an efficient way to achieve this at a city-wide scale, nearly in real time that shows traffic problems for proper urban planning, this investigation proposes a traffic index based on open government data and the use of Google Directions service. The proposed methodology showed repeatable results (p=0.7), identified temporal and spatial traffic congestion differences (p&lt;0.05), found Pearson&#8217;s correlations between the average congestion index and demographic variables population density and vehicular density (r=0.97 and r=0.91 respectively); and confirmed that urban marginalization was a significant factor (p&lt;0.05) for the average traffic congestion index, commute distances, and travel times.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[congestión vial]]></kwd>
<kwd lng="es"><![CDATA[tecnologías de la información y la comunicación]]></kwd>
<kwd lng="es"><![CDATA[planificación urbana]]></kwd>
<kwd lng="es"><![CDATA[marginación urbana]]></kwd>
<kwd lng="es"><![CDATA[ciudades inteligentes]]></kwd>
<kwd lng="en"><![CDATA[traffic congestion]]></kwd>
<kwd lng="en"><![CDATA[information and communication technologies]]></kwd>
<kwd lng="en"><![CDATA[urban planification]]></kwd>
<kwd lng="en"><![CDATA[urban marginalization]]></kwd>
<kwd lng="en"><![CDATA[smart cities]]></kwd>
</kwd-group>
</article-meta>
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