<?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>0379-3982</journal-id>
<journal-title><![CDATA[Revista Tecnología en Marcha]]></journal-title>
<abbrev-journal-title><![CDATA[Tecnología en Marcha]]></abbrev-journal-title>
<issn>0379-3982</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico de Costa Rica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0379-39822022000400138</article-id>
<article-id pub-id-type="doi">10.18845/tm.v35i4.5892</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción flujo de tráfico vehicular Ruta 27 en Costa Rica]]></article-title>
<article-title xml:lang="en"><![CDATA[Vehicle traffic flow forecasting Costa Rica highway 27]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rivera-Picado]]></surname>
<given-names><![CDATA[Cristal]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Meneses-Guzmán]]></surname>
<given-names><![CDATA[Marcela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica Ingeniera en Producción Industrial ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico de Costa Rica Escuela Ingeniería en Producción Industrial ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Costa Rica</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>35</volume>
<numero>4</numero>
<fpage>138</fpage>
<lpage>148</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S0379-39822022000400138&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S0379-39822022000400138&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S0379-39822022000400138&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El pronóstico de flujo de tráfico vehicular se considera un insumo importante para la gestión y planificación de tráfico para los sistemas de transporte inteligente (STI) de los países. En este artículo se analiza el flujo horario del tráfico de vehículos livianos que circulan en un sentido de la Ruta 27 (San José-Caldera) en Costa Rica. Se aprovechan los datos recolectados por los STI de la ruta para pronosticar el comportamiento de tráfico vehicular horario. Para ello, se proponen tres métodos de predicción, los cuales se comparan para seleccionar el modelo de mejor rendimiento: Arima Estacional (SARIMA), Ingenuo Estacional (SNAIVE)y Autoregresión con Redes Neuronales (NNAR). Los tres modelos de predicción son evaluados y se consideran útiles a la predicción, sin embargo, el modelo de NNAR tiene como resultado un mejor rendimiento al pronosticar la serie de tiempo por hora, teniendo como resultado el menor MAPE de 9.4 y se considera un candidato para ser utilizado en los STI. Al aplicar el proceso de validación cruzada en los modelos, se respalda la conclusión que conforme se prueba el modelo NNAR para más días los resultados de la predicción son más estables y precisos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Forecasting vehicle traffic flow is considered an important input for traffic planning and management for the countries' intelligent transport systems (ITS). This article analyzes the hourly flow of light vehicle traffic that drives in highway 27 of Costa Rica in one direction (San JoseCaldera). The data collected by the ITS of the route is used to forecast the behavior of hourly vehicular traffic. For this, three forecasting methods are proposed, which are compared to select the model with best performance: Seasonal Arima (SARIMA), Seasonal Naïve (SNAIVE), and Autoregression with Neural Network (NNAR). All three models are evaluated and are considered useful for prediction, however the NNAR model results in better performance when forecasting the hourly time series with the lowest MAPE of 9.4 and is consider a candidate for use in ITS. By applying the cross-validation process in the models, the conclusion is supported that as the NNAR is tested for more days, the prediction results are more stable and accurate.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Predicción de flujo de tráfico]]></kwd>
<kwd lng="es"><![CDATA[ARIMA Estacional (SARIMA)]]></kwd>
<kwd lng="es"><![CDATA[Ingenuo Estacional (SNAIVE)]]></kwd>
<kwd lng="es"><![CDATA[Autoregresión con Redes Neuronales (NNAR)]]></kwd>
<kwd lng="en"><![CDATA[Traffic flow forecasting]]></kwd>
<kwd lng="en"><![CDATA[Seasonal ARIMA(SARIAM)]]></kwd>
<kwd lng="en"><![CDATA[Seasonal Naïve (SNAIVE)]]></kwd>
<kwd lng="en"><![CDATA[Autogression with Neural Networks (NNAR)]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>(1)</label><nlm-citation citation-type="journal">
<collab>Programa Estado de la Nación</collab>
<article-title xml:lang=""><![CDATA[Estado de la Nación en Desarrollo Humano Sostenible]]></article-title>
<source><![CDATA[Transporte y Movilidad: retos en favor del desarrollo humano, Estado de la Nación, Pavas, San José,]]></source>
<year>2018</year>
</nlm-citation>
</ref>
<ref id="B2">
<label>(2)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Arguedas]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Transporte Ineficiente entraba esfuerzo tico para combatir cambio climático]]></article-title>
<source><![CDATA[Ojo al Clima.com]]></source>
<year>2019</year>
</nlm-citation>
</ref>
<ref id="B3">
<label>(3)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Laña]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Road traffic forecasting: Recent advances and new challenges]]></article-title>
<source><![CDATA[IEEE Intelligent transportation systems]]></source>
<year>2018</year>
</nlm-citation>
</ref>
<ref id="B4">
<label>(4)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zahid]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Freeway short-term travel speed prediction based on data collection time-horizons: A fast forest quantile regression approach]]></article-title>
<source><![CDATA[Approach.Sustainability,]]></source>
<year>2020</year>
<volume>12</volume>
</nlm-citation>
</ref>
<ref id="B5">
<label>(5)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Annunziato]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Urban traffic flow forecasting using neural-statistic hybrid modeling]]></article-title>
<source><![CDATA[Advances in Intelligent Systems and Computing]]></source>
<year>2013</year>
<volume>188</volume>
<page-range>183190</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>(6)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[T]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Nonlinear multivariate time-space threshold vector error correction model for short term traffic state prediction]]></article-title>
<source><![CDATA[Transportation Research Part B: Methodological,]]></source>
<year>2015</year>
<volume>76</volume>
<page-range>27-47</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>(7)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A comparative study of three multivariate short-term freeway traffic flow forecasting methods with missing data]]></article-title>
<source><![CDATA[Journal of Intelligent Transportation Systems]]></source>
<year>2016</year>
<volume>20</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>205-18</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>(8)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lv]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Traffic flow prediction with big data: a deep learning approach''approach]]></article-title>
<source><![CDATA[Transactions on Intelligent Transportation Systems]]></source>
<year>2014</year>
<volume>16</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>865-73</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>(9)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sharma]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[ANN based short-term traffic flow forecasting in undivided two lane highway]]></article-title>
<source><![CDATA[Journal of Big Data]]></source>
<year>2018</year>
<volume>5</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>1-16</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>(10)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hyndman]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Forecasting: principles and practice]]></article-title>
<source><![CDATA[OTexts]]></source>
<year>2018</year>
</nlm-citation>
</ref>
<ref id="B11">
<label>(11)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kumar]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Vanajakshi]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Short-term traffic flow prediction using seasonal ARIMA model with limited input data]]></article-title>
<source><![CDATA[European Transport Research Review]]></source>
<year>2015</year>
<volume>7</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>1-9</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>(12)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cong]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Traffic flow forecasting by a least squares support vector machine with a fruit fly optimization algorithm]]></article-title>
<source><![CDATA[European Transport Research Review]]></source>
<year>2016</year>
<volume>7</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>9</page-range></nlm-citation>
</ref>
<ref id="B13">
<label>(13)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Emargun]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Levinson]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Spatiotemporal traffic forecasting: review and proposed directions]]></article-title>
<source><![CDATA[Transport Reviews,]]></source>
<year>2018</year>
<volume>38</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>786-814</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>(14)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Luo]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Spatiotemporal traffic flow prediction with KNN and LSTM]]></article-title>
<source><![CDATA[LSTM. Journal of Advanced Transportatio]]></source>
<year>2019</year>
</nlm-citation>
</ref>
<ref id="B15">
<label>(15)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Do]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Survey of neural network&#8208;based models for short&#8208;term traffic state prediction]]></article-title>
<source><![CDATA[Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery]]></source>
<year>2019</year>
<volume>9</volume>
<numero>1</numero>
<edition>1285</edition>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B16">
<label>(16)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prediction of traffic flow combination model based on data mining]]></article-title>
<source><![CDATA[International Journal of Database Theory and Application]]></source>
<year>2015</year>
<volume>8</volume>
<numero>6</numero>
<issue>6</issue>
<page-range>303</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>(17)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Vlahogianni]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Short-term traffic forecasting: Where we are and where we're going]]></article-title>
<source><![CDATA[Transportation Research Part C: Emerging Technologies]]></source>
<year>2015</year>
<volume>43</volume>
<page-range>3-19</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>(18)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hyndman]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A forecast ensemble benchmark]]></article-title>
<source><![CDATA[OTexts]]></source>
<year>2018</year>
</nlm-citation>
</ref>
<ref id="B19">
<label>(19)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cordero]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Sistemas de transporte inteligente-conduciendo hacia futuro Centroamérica]]></article-title>
<source><![CDATA[Lanner.com]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B20">
<label>(20)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[McLaughlin]]></surname>
<given-names><![CDATA[R.L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Forecasting models: Sophisticated or naive]]></article-title>
<source><![CDATA[Journal of Forecasting]]></source>
<year>1983</year>
<volume>2</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>274</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>(21)</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Thoplan]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Simple v/s Sophisticated Methods of Forecasting for Mauritius Monthly Tourist Arrival Data]]></article-title>
<source><![CDATA[International Journal of Statistics and Applications]]></source>
<year>2014</year>
<volume>4</volume>
<page-range>217-23</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
