{"id":21607,"date":"2023-02-23T11:27:44","date_gmt":"2023-02-23T11:27:44","guid":{"rendered":"https:\/\/cit.upc.edu\/?post_type=portfolio&p=21607"},"modified":"2023-07-13T09:46:12","modified_gmt":"2023-07-13T09:46:12","slug":"mobilitapp-deep-learning-para-predecir-el-modo-de-transporte-utilizado-por-la-ciudadania-y-proteger-sus-datos-de-forma-segura","status":"publish","type":"portfolio","link":"https:\/\/cit.upc.edu\/es\/portfolio-item\/mobilitapp-deep-learning-para-predecir-el-modo-de-transporte-utilizado-por-la-ciudadania-y-proteger-sus-datos-de-forma-segura\/","title":{"rendered":"MobilitApp: Deep learning para predecir el modo de transporte utilizado por la ciudadan\u00eda y proteger sus datos de forma segura"},"content":{"rendered":"\n

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23\/02\/2023
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MobilitApp es una aplicaci\u00f3n capaz de predecir el modo de transporte empleado por la ciudadan\u00eda mediante una herramienta basada en aprendizaje profundo a partir de tres sensores del tel\u00e9fono m\u00f3vil.\r
MobilitApp incluye algoritmos que permiten recoger datos de los trayectos multimodales (cambios de modos de transporte) con total privacidad. Esto ayudar\u00e1 a analizar f\u00e1cilmente los flujos y h\u00e1bitos de movilidad de la ciudadan\u00eda para saber de d\u00f3nde a d\u00f3nde se mueven los usuarios, y d\u00f3nde y cu\u00e1ndo se producen los cambios de modo de transporte, para ofrecer un mejor servicio del transporte p\u00fablico, y a la vez potenciar una movilidad m\u00e1s sostenible.\r
La aplicaci\u00f3n se ha dise\u00f1ado para la ciudad de Barcelona en colaboraci\u00f3n con la Autoridad del Transporte Metropolitano (ATM). Durante el mes de febrero de 2023, se est\u00e1 iniciando la integraci\u00f3n de la aplicaci\u00f3n dentro del c\u00f3digo de la aplicaci\u00f3n MOU-TE de ATM y, para el mes de septiembre, se prev\u00e9 iniciar las pruebas piloto en colaboraci\u00f3n con UPC Sostenible, a fin de analizar los h\u00e1bitos de movilidad y la huella de poluci\u00f3n del estudiantado voluntario de toda la UPC.
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MobilitApp usa tres sensores: el aceler\u00f3metro, el magnet\u00f3metro y el giroscopio. Tambi\u00e9n lee una muestra GPS al inicio y al final de cada trayecto, y en cada cambio de modo de transporte detectado. El modo de transporte empleado se predice con un acierto del 98%, mediante un modelo de aprendizaje profundo, entrenado con una base de datos suficientemente grande y representativa. Esta base de datos recoge trayectos unimodales etiquetados por personas voluntarias en desplazamientos, utilizando 12 modos de transporte: metro, bus, tranv\u00eda, tren, bicicleta, bicicleta el\u00e9ctrica, patinete el\u00e9ctrico, correr, andar, moto, coche, incluso el reposo.\r
MobilitApp ayudar\u00e1 a estudiar las combinaciones entre medios de transporte (e-scooter + bus, bicing + metro, etc). El an\u00e1lisis del flujo de movilidad se podr\u00e1 disgregar por diferentes variables, como g\u00e9nero, rango de edad, franjas temporales, d\u00eda de la semana y zonas del mapa, entre otras.\r
MobilitApp<\/a>, disponible en Android y pronto tambi\u00e9n en iOS, ha sido desarrollada por el grupo de investigaci\u00f3n Smart Services for Information Systems and Communication Network (SISCOM<\/a>) de la Universitat Polit\u00e8cnica de Catalunya – BarcelonaTech (UPC), en colaboraci\u00f3n con ATM<\/a>. Est\u00e1 financiada por el proyecto MOBILYTICS<\/a> (Anonymization technology for AI-based analytics of mobility data, TED2021-129782B-I00, proyectos estrat\u00e9gicos orientados a la transici\u00f3n ecol\u00f3gica y a la transici\u00f3n digital); y por el proyecto COMPROMISE<\/a> (Enhancing communication protocols with machine learning while protecting sensitive data, PID2020-113795RB-C31\/AEI\/10.13039\/501100011033), ambos del Ministerio de Ciencia e Innovaci\u00f3n.
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\"MobilitApp\"
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Tecnolog\u00eda<\/h5>\r
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Log\u00edstica y Movilidad<\/a>
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Telecom y desarolladores software<\/a>\r
Transporte y movilidad<\/a>
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Tema<\/h5>\r
Mobile Technologies<\/a>
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\u00bfQuieres saber m\u00e1s?<\/h5>
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21607

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MobilitApp es una aplicaci\u00f3n capaz de predecir el modo de transporte empleado por la ciudadan\u00eda mediante una herramienta basada en aprendizaje profundo a partir de tres sensores del tel\u00e9fono m\u00f3vil.<\/p>\n","protected":false},"author":5,"featured_media":21634,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":[],"portfolio-types":[432,76,421,365,363],"class_list":["post-21607","portfolio","type-portfolio","status-publish","has-post-thumbnail","hentry","portfolio-types-mobile","portfolio-types-tecnologias-de-las-tic","portfolio-types-tecnologias-de-logistica-y-movilidad","portfolio-types-telecom-y-software","portfolio-types-transporte-y-movilidad"],"yoast_head":"\nDeep learning para predecir el modo de transporte - CIT UPC<\/title>\n<meta name=\"description\" content=\"MobilitApp es una aplicaci\u00f3n capaz de predecir el modo de transporte empleado por la ciudadan\u00eda mediante una herramienta basada en aprendizaje profundo a partir de tres sensores del tel\u00e9fono m\u00f3vil.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cit.upc.edu\/es\/portfolio-item\/mobilitapp-deep-learning-para-predecir-el-modo-de-transporte-utilizado-por-la-ciudadania-y-proteger-sus-datos-de-forma-segura\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep learning para predecir el modo de transporte - 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