{"id":5442,"date":"2026-10-13T12:00:22","date_gmt":"2026-10-13T12:00:22","guid":{"rendered":"https:\/\/catlec.cl\/?p=5442"},"modified":"2026-10-13T20:15:32","modified_gmt":"2026-10-13T20:15:32","slug":"investigador-catlec-propone-nuevo-enfoque-para-comprender-como-elegimos-nuestro-modo-de-transporte","status":"publish","type":"post","link":"https:\/\/catlec.cl\/en-us\/investigador-catlec-propone-nuevo-enfoque-para-comprender-como-elegimos-nuestro-modo-de-transporte\/","title":{"rendered":"Investigador CATLEC propone nuevo enfoque para comprender c\u00f3mo elegimos nuestro modo de transporte"},"content":{"rendered":"\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong>Una publicaci\u00f3n liderada por el investigador asociado CATLEC y acad\u00e9mico de la Universidad de Concepci\u00f3n, Patricio Salas, desarroll\u00f3 un enfoque que combina informaci\u00f3n de viajes realizados, rutas generadas mediante herramientas digitales,<\/strong> <strong><em>machine learning<\/em><\/strong> <strong>e Inteligencia Artificial Explicable para comprender mejor qu\u00e9 factores se relacionan con la elecci\u00f3n de un medio de transporte.&nbsp;<\/strong><\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Un equipo de la Universidad de Concepci\u00f3n desarroll\u00f3 una metodolog\u00eda para enriquecer el an\u00e1lisis de c\u00f3mo las personas deciden entre caminar, utilizar transporte p\u00fablico, autom\u00f3vil, bicicleta u otras alternativas para realizar sus viajes.&nbsp;<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">El art\u00edculo, titulado <em><a href=\"https:\/\/doi.org\/10.1177\/03611981261444331\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">Enhancing Travel Mode Choice Modeling with Route-Based Attributes and Explainable Machine Learning<\/mark><\/a><\/em>, fue desarrollado por\u00a0 el investigador CATLEC <strong>Patricio Salas,<\/strong> junto a los investigadores del <a href=\"https:\/\/estadistica.udec.cl\/\" target=\"_blank\" rel=\"noreferrer noopener\">Departamento de Estad\u00edstica UdeC<\/a> <strong>Patricio S\u00e1ez y Lilian Barr\u00eda<\/strong>, y combin\u00f3 conocimientos de transporte, estad\u00edstica, <em>machine learning<\/em> e Inteligencia Artificial Explicable para analizar los factores asociados a la elecci\u00f3n de modos de transporte.\u00a0<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">\u201cLa investigaci\u00f3n aborda una limitaci\u00f3n habitual al estudiar c\u00f3mo se eligen los modos de transporte: gran parte de la informaci\u00f3n disponible proviene de encuestas en las que se reportan caracter\u00edsticas de viajes, tiempos o distancias, pero estos datos pueden contener imprecisiones y, normalmente, s\u00f3lo describen el modo de transporte utilizado, sin entregar informaci\u00f3n sobre las alternativas que se podr\u00edan haber elegido\u201d, explica el investigador CATLEC, Patricio Salas.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Para superar esa limitaci\u00f3n, el equipo complement\u00f3 informaci\u00f3n proveniente de una encuesta de movilidad con datos obtenidos mediante herramientas digitales de planificaci\u00f3n de viajes. As\u00ed, fue posible incorporar caracter\u00edsticas de las rutas disponibles para diferentes modos de transporte, incluyendo aspectos como su nivel de rectitud, sinuosidad o cambios de direcci\u00f3n.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">El an\u00e1lisis se aplic\u00f3 a 1.372 viajes correspondientes a un contexto universitario en Hamilton, Canad\u00e1. La informaci\u00f3n proven\u00eda de una encuesta de preferencias reveladas, es decir, basada en decisiones de transporte que las personas realizaron. Para cada viaje, los investigadores generaron adem\u00e1s alternativas de&nbsp; desplazamiento mediante Google Maps para caminar, utilizar transporte p\u00fablico, autom\u00f3vil o bicicleta.&nbsp;<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">\u201cEl principal hallazgo fue demostrar que incorporar informaci\u00f3n sobre la ruta disponible para cada alternativa de viaje permite comprender mejor por qu\u00e9 las personas eligen un determinado modo de transporte\u201d, concluye Salas.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">La revista <em>Transportation Research Record: Journal of the Transportation Research Board (TRR)<\/em> public\u00f3 el estudio, que aporta evidencia de que las caracter\u00edsticas del recorrido pueden entregar informaci\u00f3n complementaria a variables tradicionalmente utilizadas, como los tiempos de viaje, las distancias o las caracter\u00edsticas de las personas.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-medium-font-size\">Datos, movilidad e IA para comprender las decisiones de viaje&nbsp;<\/h2>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">La metodolog\u00eda combin\u00f3 distintas disciplinas y herramientas. Primero, los investigadores tomaron informaci\u00f3n de viajes realizados y la complementaron con datos de las rutas generadas por un planificador digital para cada alternativa de transporte. Esto permiti\u00f3 caracterizar elementos del recorrido que habitualmente no est\u00e1n disponibles en una encuesta tradicional.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Posteriormente, el equipo utiliz\u00f3 cuatro algoritmos de <em>machine learning<\/em> basados en \u00e1rboles para identificar patrones asociados a la elecci\u00f3n del modo de transporte. Sobre esos modelos se aplicaron t\u00e9cnicas de Inteligencia Artificial Explicable, principalmente SHAP y LIME, herramientas que permiten examinar qu\u00e9 variables est\u00e1n influyendo en los resultados obtenidos tanto a nivel general como en viajes individuales.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">\u201cAl combinar informaci\u00f3n tradicional con datos m\u00e1s detallados sobre las rutas disponibles y utilizar algoritmos de Machine Learning, es posible aprovechar de mejor manera el creciente volumen y diversidad de informaci\u00f3n que hoy se genera sobre la movilidad\u201d, asegura el investigador.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">A su vez, conf\u00eda en que la investigaci\u00f3n ayude a instituciones p\u00fablicas en \u00e1reas como planificaci\u00f3n del transporte, dise\u00f1o de infraestructura, promoci\u00f3n de modos m\u00e1s sostenibles y evaluaci\u00f3n de alternativas de movilidad.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">El siguiente paso de esta l\u00ednea de investigaci\u00f3n ser\u00e1 evaluar la metodolog\u00eda con mayores vol\u00famenes de informaci\u00f3n y en otros contextos y ciudades. La proyecci\u00f3n planteada por el equipo es continuar integrando <em>machine learning<\/em>, Inteligencia Artificial Explicable y modelos tradicionales de elecci\u00f3n para combinar capacidad predictiva con una mejor comprensi\u00f3n del comportamiento de viaje.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Accede a la investigaci\u00f3n completa aqu\u00ed: <a href=\"https:\/\/catlec.cl\/enhancing-travel-mode-choice-modeling-with-route-based-attributes-and-explainable-machine-learning\/\">https:\/\/catlec.cl\/enhancing-travel-mode-choice-modeling-with-route-based-attributes-and-explainable-machine-learning\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Una publicaci\u00f3n liderada por el investigador asociado CATLEC y acad\u00e9mico de la Universidad de Concepci\u00f3n, Patricio Salas, desarroll\u00f3 un enfoque<\/p>\n","protected":false},"author":4,"featured_media":5444,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[80,51],"tags":[136,147,105],"class_list":["post-5442","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-investigacion-noticias","category-noticias","tag-patricio-salas","tag-transporte-para-ciudades-habitables","tag-universidad-de-concepcion"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Investigador CATLEC propone nuevo enfoque para comprender c\u00f3mo elegimos nuestro modo de transporte - CATLEC<\/title>\n<meta name=\"description\" content=\"Investigador CATLEC aplica machine learning e IA explicable para analizar c\u00f3mo las personas eligen su modo de transporte.\" \/>\n<meta name=\"robots\" content=\"index, follow, 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