{"id":5315,"date":"2026-09-08T14:05:43","date_gmt":"2026-09-08T14:05:43","guid":{"rendered":"https:\/\/catlec.cl\/?p=5315"},"modified":"2026-09-21T15:38:08","modified_gmt":"2026-09-21T15:38:08","slug":"enhancing-travel-mode-choice-modeling-with-route-based-attributes-and-explainable-machine-learning","status":"publish","type":"post","link":"https:\/\/catlec.cl\/en-us\/enhancing-travel-mode-choice-modeling-with-route-based-attributes-and-explainable-machine-learning\/","title":{"rendered":"Enhancing Travel Mode-Choice Modeling with Route-Based Attributes and Explainable Machine Learning"},"content":{"rendered":"\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong>Fecha:<\/strong>\u00a003\/07\/2026<br><strong>Autores:<\/strong>\u00a0Patricio Salas <sup>ab<\/sup>,\u00a0Patricio S\u00e1ez\u00a0<sup>a<\/sup>,\u00a0Lilian Barr\u00eda\u00a0<sup>a<\/sup><\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><sup>a<\/sup>Department of Statistics, Universidad de Concepci\u00f3n, Concepci\u00f3n Chile<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><sup>b<\/sup><a href=\"https:\/\/catlec.cl\/\" data-type=\"link\" data-id=\"https:\/\/catlec.cl\/\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">Center for Advanced Transportation, Logistics, and Economic Competitiveness (CATLEC)<\/mark><\/a>, ANID\/CIN 250061, Santiago, Chile<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong>Revista cient\u00edfica \/ editorial:<\/strong> <a href=\"https:\/\/journals.sagepub.com\/home\/TRR\" target=\"_blank\" rel=\"noreferrer noopener\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">Transportation Research Record: Journal of the Transportation Research Board<\/mark><\/a><\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong>Abstract o breve resumen:<\/strong> Modelar la elecci\u00f3n del modo de transporte es esencial para dise\u00f1ar sistemas de movilidad eficientes y sostenibles. Las encuestas de preferencias reveladas proporcionan informaci\u00f3n valiosa, pero se basan en datos autoinformados, que pueden estar sesgados y que, por lo general, no est\u00e1n disponibles para las alternativas no elegidas. Este estudio propone un proceso anal\u00edtico integrado que combina datos de encuestas de preferencias reveladas, atributos a nivel de ruta derivados de un planificador digital de viajes, clasificadores de aprendizaje autom\u00e1tico y m\u00e9todos de inteligencia artificial explicable (XAI) para evaluar de manera conjunta el desempe\u00f1o predictivo y la interpretaci\u00f3n del comportamiento. Utilizando un conjunto de datos de 1,372 viajes recopilados en el contexto de los desplazamientos universitarios como aplicaci\u00f3n ilustrativa, las respuestas de la encuesta se enriquecieron con tiempos de viaje espec\u00edficos por modo de transporte y caracter\u00edsticas geom\u00e9tricas de las rutas recomendadas por el planificador, obtenidas a partir de la interfaz de programaci\u00f3n de aplicaciones (API) de Google Maps. Se evaluaron cuatro clasificadores basados en \u00e1rboles en un marco de validaci\u00f3n sin fugas, y el comportamiento del modelo se interpret\u00f3 utilizando SHAP (SHapley Additive exPlanations) y LIME (Local Interpretable Model-agnostic Explanations) tanto a nivel global como local. Los resultados indican que, cuando se combinaron los atributos geom\u00e9tricos a nivel de ruta con los datos de la encuesta de preferencias reveladas, la precisi\u00f3n predictiva se mantuvo comparable, mientras que la interpretabilidad mejor\u00f3 sustancialmente. Los an\u00e1lisis de XAI revelaron que caracter\u00edsticas de la ruta como la rectitud, la sinuosidad y la desviaci\u00f3n angular se destacaron como predictores significativos que modulaban el esfuerzo percibido durante el viaje, particularmente para caminar y el transporte p\u00fablico, a pesar de su impacto limitado en el desempe\u00f1o agregado.<\/p>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong>Enlace al documento o DOI:<\/strong> <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\">https:\/\/doi.org\/10.1177\/03611981261444331<\/mark><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Fecha:\u00a003\/07\/2026Autores:\u00a0Patricio Salas ab,\u00a0Patricio S\u00e1ez\u00a0a,\u00a0Lilian Barr\u00eda\u00a0a aDepartment of Statistics, Universidad de Concepci\u00f3n, Concepci\u00f3n Chile bCenter for Advanced Transportation, Logistics, and Economic<\/p>\n","protected":false},"author":4,"featured_media":5345,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[87,50],"tags":[],"class_list":["post-5315","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articulos-cientificos","category-publicaciones"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Enhancing Travel Mode-Choice Modeling with Route-Based Attributes and Explainable Machine Learning - CATLEC<\/title>\n<meta name=\"description\" content=\"Estudio aplica aprendizaje autom\u00e1tico explicable para analizar c\u00f3mo los atributos de ruta influyen en la elecci\u00f3n modal en transporte.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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