{"id":8709,"date":"2022-07-08T00:00:00","date_gmt":"2022-07-07T22:00:00","guid":{"rendered":"https:\/\/i2cat.net\/la-fundacio-i2cat-lidera-el-desenvolupament-dun-sistema-de-deteccio-de-riscos-i-fluxos-de-transit-basat-en-intelligencia-artificial-i-big-data-en-una-iniciativa-de-cidai\/"},"modified":"2025-07-23T12:17:51","modified_gmt":"2025-07-23T10:17:51","slug":"la-fundacio-i2cat-lidera-el-desenvolupament-dun-sistema-de-deteccio-de-riscos-i-fluxos-de-transit-basat-en-intelligencia-artificial-i-big-data-en-una-iniciativa-de-cidai","status":"publish","type":"post","link":"https:\/\/i2cat.net\/ca\/la-fundacio-i2cat-lidera-el-desenvolupament-dun-sistema-de-deteccio-de-riscos-i-fluxos-de-transit-basat-en-intelligencia-artificial-i-big-data-en-una-iniciativa-de-cidai\/","title":{"rendered":"La Fundaci\u00f3 i2CAT lidera el desenvolupament d’un sistema de detecci\u00f3 de riscos i fluxos de tr\u00e0nsit basat en Intel\u00b7lig\u00e8ncia Artificial i big data en una iniciativa de CIDAI"},"content":{"rendered":"

Investigadors de la Fundaci\u00f3 i2CAT<\/a>, centre de recerca aplicada en l’\u00e0mbit de les noves tecnologies digitals, han liderat el desenvolupament d’un sistema per a la detecci\u00f3 de riscos i fluxos de tr\u00e0nsit en vies urbanes i interurbanes basat en l’\u00fas de tecnologies d’Intel\u00b7lig\u00e8ncia Artificial (IA) i big data<\/em>. El projecte \u00e9s una iniciativa del Centre of Innovation for Data tech and Artificial Intelligence <\/em>(CIDAI)<\/a> i en ell han participat, tamb\u00e9, investigadors del Barcelona Supercomputing Center<\/em> (BSC)<\/a>, del Centre de Telecomunicacions i Tecnologies de la Informaci\u00f3 (CTTI)<\/a> i de Eurecat \u2013 Centre Tecnol\u00f2gic de Catalunya<\/a>.<\/p>\n

El sistema proporciona solucions de mobilitat intel\u00b7ligent basades en IA i big data<\/em> sobre la base de dos objectius principals. El primer d’ells \u00e9s aconseguir la prevenci\u00f3 proactiva d’accidents de tr\u00e0nsit mitjan\u00e7ant la identificaci\u00f3 de situacions de risc que impliquen tant vehicles com a vianants. El segon objectiu s’ha centrat en analitzar el flux de tr\u00e0nsit en zones urbanes i segments de carreteres interurbanes amb la finalitat de detectar comportaments an\u00f2mals i obtenir informaci\u00f3 sobre les condicions de tr\u00e0nsit que acaben derivant en embussos.<\/p>\n

Plataforma software<\/em> per a la identificaci\u00f3 de riscos<\/strong>
\nPer aconseguir aquests objectius, els investigadors van seleccionar dues ubicacions interurbanes en dos segments de la carretera C31 i una ubicaci\u00f3 urbana, per a la qual es va triar un encreuament a Barcelona. Mitjan\u00e7ant un sistema de c\u00e0meres, es van registrar m\u00e9s de 150 hores de v\u00eddeo en aquests tres punts. A partir d’aquest material, es va identificar un conjunt de casos d’\u00fas representatius que abastaven escenaris de risc t\u00edpics causats, per exemple, per vehicles que canvien de carril sense respectar les l\u00ednies cont\u00ednues o el pas de vianants amb el sem\u00e0for en vermell.<\/p>\n

A\u00a0continuaci\u00f3, els investigadors van desenvolupar sobre un cloud<\/em> h\u00edbrid (part de la infraestructura en Azure<\/a>, gr\u00e0cies a la donaci\u00f3 de cr\u00e8dits per part de Microsoft<\/a>, i part en sistemes on-premise<\/em>) una plataforma de software<\/em> per al processament de dades i van elaborar els m\u00f2duls d\u2019an\u00e0lisis. Es va desenvolupar un m\u00f2dul de detecci\u00f3 i seguiment d’objectes basat en tecnologia IA per a la detecci\u00f3 dels diferents tipus d’usuaris de la via (cotxes, camions, motos, vianants, etc.) seguint-los a trav\u00e9s de quadres consecutius i extraient informaci\u00f3 valuosa sobre la seva posici\u00f3 i velocitat. Tamb\u00e9 es va elaborar un m\u00f2dul de detecci\u00f3 de sem\u00e0fors i es van establir les regles i mapes sem\u00e0ntics per detectar riscos espec\u00edfics associats al tr\u00e0nsit.<\/p>\n

Un model de prevenci\u00f3 basat en les noves tecnologies<\/strong>
\nEls investigadors destaquen el bon rendiment del sistema, que ha perm\u00e8s la detecci\u00f3 i el seguiment de m\u00faltiples tipus de vehicles en diferents moments del dia i l’estimaci\u00f3 de la velocitat, un element de gran import\u00e0ncia perqu\u00e8 permet determinar, per exemple, la dist\u00e0ncia de seguretat o la identificaci\u00f3 de vehicles detinguts.<\/p>\n

\"\"
Exemple de risc urb\u00e0 detectat en un encreuament de Barcelona<\/figcaption><\/figure>\n

El cas de canvi il\u00b7legal de carril va ser detectat amb una precisi\u00f3 del 89% i el cas de pas de vianants amb sem\u00e0for en vermell amb un 75%. Un altre indicador significatiu \u00e9s el baix nombre de deteccions de falsos negatius. El sistema va identificar correctament el 93,75% de les situacions de risc i fins i tot va permetre detectar situacions de risc alt que no estaven contemplades a l’inici del projecte com, per exemple, la circulaci\u00f3 de bicicletes i de vianants pels laterals dels carrils de la carretera.<\/p>\n

Quant als tipus de risc, el sistema ha posat en evid\u00e8ncia la difer\u00e8ncia entre els entorns interurbans, que involucren principalment a autom\u00f2bils i vehicles m\u00e9s grans com a camions i autobusos, i els entorns urbans, on s\u00f3n els vianants els que corren un risc major en la majoria d’escenaris.<\/p>\n

La informaci\u00f3 obtinguda pel sistema podria ajudar les administracions a desenvolupar models de prevenci\u00f3 per reduir substancialment el nombre d’accidents de tr\u00e0nsit i de morts en la carretera. Al seu torn, permetria millores substancials en el flux de tr\u00e0nsit i la reducci\u00f3 d’emissions de gasos contaminants, gr\u00e0cies a la reducci\u00f3 dels embussos i a l’optimitzaci\u00f3 del temps d’espera en els sem\u00e0fors a les ciutats.<\/p>\n

\u201cLes administracions i les autoritats de tr\u00e0nsit de molts pa\u00efsos ja estan plantejant pels pr\u00f2xims anys una nova estrat\u00e8gia de seguretat vi\u00e0ria i gesti\u00f3 del tr\u00e0nsit basada en la transformaci\u00f3 digital i en l’explotaci\u00f3 de dades procedents de diferents fonts. Per a aix\u00f2, ser\u00e0 necessari implementar solucions tecnol\u00f2giques com la desenvolupada en aquest projecte\u201d, conclouen els investigadors.<\/p>\n","protected":false},"excerpt":{"rendered":"

Investigadors de la Fundaci\u00f3 i2CAT, centre de recerca aplicada en l’\u00e0mbit de les noves tecnologies…<\/p>\n","protected":false},"author":1,"featured_media":8713,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-8709","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","area-artificial-intelligence-ai","area-social-impact","collection-research","collection-social-and-territorial-impact"],"acf":[],"yoast_head":"\nLa Fundaci\u00f3 i2CAT lidera el desenvolupament d'un sistema de detecci\u00f3 de riscos i fluxos de tr\u00e0nsit basat en Intel\u00b7lig\u00e8ncia Artificial i big data en una iniciativa de CIDAI - i2CAT Research Centre<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/i2cat.net\/ca\/la-fundacio-i2cat-lidera-el-desenvolupament-dun-sistema-de-deteccio-de-riscos-i-fluxos-de-transit-basat-en-intelligencia-artificial-i-big-data-en-una-iniciativa-de-cidai\/\" \/>\n<meta property=\"og:locale\" content=\"ca_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"La Fundaci\u00f3 i2CAT lidera el desenvolupament d'un sistema de detecci\u00f3 de riscos i fluxos de tr\u00e0nsit basat en Intel\u00b7lig\u00e8ncia Artificial i big data en una iniciativa de CIDAI - 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