{"id":21613,"date":"2023-02-23T11:27:00","date_gmt":"2023-02-23T11:27:00","guid":{"rendered":"https:\/\/cit.upc.edu\/?post_type=portfolio&p=21613"},"modified":"2023-06-30T08:43:13","modified_gmt":"2023-06-30T08:43:13","slug":"mobilitapp-deep-learning-per-predir-el-mode-de-transport-utilitzat-per-la-ciutadania-i-protegir-les-seves-dades-de-manera-segura","status":"publish","type":"portfolio","link":"https:\/\/cit.upc.edu\/ca\/portfolio-item\/mobilitapp-deep-learning-per-predir-el-mode-de-transport-utilitzat-per-la-ciutadania-i-protegir-les-seves-dades-de-manera-segura\/","title":{"rendered":"MobilitApp: Deep learning per predir el mode de transport utilitzat per la ciutadania i protegir les seves dades de manera segura"},"content":{"rendered":"\n

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23\/02\/2023
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MobilitApp \u00e9s una aplicaci\u00f3 capa\u00e7 de predir el mode de transport emprat per la ciutadania mitjan\u00e7ant una eina basada en aprenentatge profund\u00a0 a partir de tres sensors del tel\u00e8fon m\u00f2bil.\r
MobilitApp inclou algorismes que permeten recollir dades dels trajectes multimodals (canvis de modes de transport) amb total privadesa. Aix\u00f2 ajudar\u00e0 a analitzar f\u00e0cilment els fluxes i h\u00e0bits de mobilitat de la ciutadania per saber d\\’on a on es mouen els usuaris, i a on i quan es produeixen els canvis de mode de transport oferir un millor servei del transport p\u00fablic, i alhora potenciar una mobilitat m\u00e9s sostenible.\r
L\u2019aplicaci\u00f3 s\u2019ha dissenyat per a la ciutat de Barcelona en col\u00b7laboraci\u00f3 amb l\u2019Autoritat del Transport Metropolit\u00e0 (ATM). Durant el mes de febrer de 2023, s\u2019est\u00e0 iniciant la integraci\u00f3 de l\u2019aplicaci\u00f3 dins del codi de l\u2019aplicaci\u00f3 MOU-TE de l\u2019ATM i per al mes de setembre, es preveu iniciar les proves pilot en col\u00b7laboraci\u00f3 amb UPC Sostenible, per analitzar els h\u00e0bits de mobilitat i la petjada de pol\u00b7luci\u00f3 de l\\’estudiantat voluntari de tota la UPC.
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MobilitApp fa servir tres sensors: l\u2019acceler\u00f2metre, el magnet\u00f2metre i el giroscopi. Tamb\u00e9 llegeix una mostra GPS a l\u2019inici i al final de cada trajecte, i a cada canvi de mode de transport detectat. El mode de transport emprat es prediu amb un encert del 98%, mitjan\u00e7ant un model d\u2019aprenentatge profund, entrenat amb una base de dades suficientment gran i representativa. Aquesta base de dades recull trajectes unimodals etiquetats per persones volunt\u00e0ries en despla\u00e7aments, fent servir 12 modes de transport: metro, bus, tranvia, tren, bicicleta, bicicleta el\u00e8ctrica, patinet el\u00e8ctric, c\u00f3rrer, caminar, moto, cotxe, incl\u00f2s el rep\u00f2s.\u00a0\r
MobilitApp ajudar\u00e0 a estudiar les combinacions entre mitjans de transport (e-scooter + bus, bicing + metro, etc). L\\’an\u00e0lisi del flux de mobilitat es podr\u00e0 disgregar per diferents variables com ara g\u00e8nere, rang d\u2019edat, franges temporals, dia de la setmana i zones del mapa, entre d\u2019altres.\r
MobilitApp<\/a>, disponible en Android i aviat tamb\u00e9 en iOS, ha estat desenvolupada pel grup de recerca Smart Services for Information Systems and Communication Network (SISCOM<\/a>) de la Universitat Polit\u00e8cnica de Catalunya – BarcelonaTech (UPC), en col\u00b7laboraci\u00f3 amb l\u2019ATM<\/a>. \u00c9s finan\u00e7ada pel projecte 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); i pel projecte COMPROMISE<\/a> (Enhancing communication protocols with machine learning while protecting sensitive data, PID2020-113795RB-C31\/AEI\/10.13039\/501100011033), ambd\u00f3s del Ministerio de Ciencia e Innovaci\u00f3n.
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\"MobilitApp\"
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Tecnologia<\/h5>\r
TIC<\/a>\r
Log\u00edstica i Mobilitat<\/a>
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Sector<\/h5>\r
Telecom i desenvolupadors de software<\/a>\r
Transport i mobilitat<\/a>
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Tema<\/h5>\r
Mobile Technologies<\/a>
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Vols saber m\u00e9s?<\/h5>
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21613

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MobilitApp \u00e9s una aplicaci\u00f3 capa\u00e7 de predir el mode de transport emprat per la ciutadania mitjan\u00e7ant una eina basada en aprenentatge profund a partir de tres sensors del tel\u00e8fon m\u00f2bil.<\/p>\n","protected":false},"author":5,"featured_media":21628,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":[],"portfolio-types":[910,221,911,410,412],"class_list":["post-21613","portfolio","type-portfolio","status-publish","has-post-thumbnail","hentry","portfolio-types-mobile-ca","portfolio-types-tecnologias-de-las-tic-ca","portfolio-types-tecnologias-de-logistica-y-movilidad-ca","portfolio-types-telecom-i-desenvolupadors-de-programari","portfolio-types-transport-i-mobilitat"],"yoast_head":"\nDeep learning per predir el mode de transport - CIT UPC<\/title>\n<meta name=\"description\" content=\"MobilitApp \u00e9s una aplicaci\u00f3 capa\u00e7 de predir el mode de transport emprat per la ciutadania mitjan\u00e7ant una eina basada en aprenentatge profund a partir de tres sensors del tel\u00e8fon m\u00f2bil.\" \/>\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\/ca\/portfolio-item\/mobilitapp-deep-learning-per-predir-el-mode-de-transport-utilitzat-per-la-ciutadania-i-protegir-les-seves-dades-de-manera-segura\/\" \/>\n<meta property=\"og:locale\" content=\"ca_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep learning per predir el mode de transport - 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