{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T12:30:32Z","timestamp":1769603432792,"version":"3.49.0"},"reference-count":23,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund (FEDER)","award":["POCI-01-0247-FEDER-069989"],"award-info":[{"award-number":["POCI-01-0247-FEDER-069989"]}]},{"name":"European Regional Development Fund (FEDER)","award":["CENTRO-01-0246-FEDER-000008"],"award-info":[{"award-number":["CENTRO-01-0246-FEDER-000008"]}]},{"name":"European Regional Development Fund (FEDER)","award":["FAPESC 1378\/2021"],"award-info":[{"award-number":["FAPESC 1378\/2021"]}]},{"name":"European Structural Investment Funds (ESIF)","award":["POCI-01-0247-FEDER-069989"],"award-info":[{"award-number":["POCI-01-0247-FEDER-069989"]}]},{"name":"European Structural Investment Funds (ESIF)","award":["CENTRO-01-0246-FEDER-000008"],"award-info":[{"award-number":["CENTRO-01-0246-FEDER-000008"]}]},{"name":"European Structural Investment Funds (ESIF)","award":["FAPESC 1378\/2021"],"award-info":[{"award-number":["FAPESC 1378\/2021"]}]},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Estado de Santa Catarina","award":["POCI-01-0247-FEDER-069989"],"award-info":[{"award-number":["POCI-01-0247-FEDER-069989"]}]},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Estado de Santa Catarina","award":["CENTRO-01-0246-FEDER-000008"],"award-info":[{"award-number":["CENTRO-01-0246-FEDER-000008"]}]},{"name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa e Inova\u00e7\u00e3o do Estado de Santa Catarina","award":["FAPESC 1378\/2021"],"award-info":[{"award-number":["FAPESC 1378\/2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.<\/jats:p>","DOI":"10.3390\/s22124485","type":"journal-article","created":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T01:39:54Z","timestamp":1655257194000},"page":"4485","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5128-2253","authenticated-orcid":false,"given":"Fernando Jos\u00e9","family":"Braz","sequence":"first","affiliation":[{"name":"Instituto Federal Catarinense Campus Araquari, Araquari 89245-000, Brazil"}]},{"given":"Jo\u00e3o","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Departamento de Electr\u00f3nica Telecomunica\u00e7\u00f5es e Inform\u00e1tica e Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"given":"Francisco","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Departamento de Electr\u00f3nica Telecomunica\u00e7\u00f5es e Inform\u00e1tica e Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0880-3038","authenticated-orcid":false,"given":"Kawan","family":"Weege","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade do Estado de Santa Catarina, Florianopolis 88035-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6634-6213","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Almeida","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6452-1900","authenticated-orcid":false,"given":"Fabiano","family":"Baldo","sequence":"additional","affiliation":[{"name":"Departamento de Ci\u00eancia da Computa\u00e7\u00e3o, Universidade do Estado de Santa Catarina, Florianopolis 88035-901, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7696-4231","authenticated-orcid":false,"given":"Pedro","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Escola Superior de Tecnologia e Gest\u00e3o de \u00c1gueda e Instituto de Telecomunica\u00e7\u00f5es, Universidade de Aveiro, 3810-193 Aveiro, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.arcontrol.2017.03.005","article-title":"Traffic state estimation on highway: A comprehensive survey","volume":"43","author":"Seo","year":"2017","journal-title":"Annu. 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