dc.contributor.author | Belhadi, Asma | |
dc.contributor.author | Djenouri, Youcef | |
dc.contributor.author | Djenouri, Djamel | |
dc.contributor.author | Lin, Jerry Chun-Wei | |
dc.date.accessioned | 2021-04-30T18:48:54Z | |
dc.date.available | 2021-04-30T18:48:54Z | |
dc.date.created | 2020-07-29T11:25:27Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Applied intelligence (Boston). 2020, 50 3252-3265. | en_US |
dc.identifier.issn | 0924-669X | |
dc.identifier.uri | https://hdl.handle.net/11250/2740676 | |
dc.description.abstract | This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced. A recurrent neural network approach, named RNN-LF, is then proposed to predict the long-term of flows from multiple data sources. Moreover, a parallel implementation on GPU of the proposed solution is developed (GRNN-LF), which allows to boost the performance of RNN-LF. Several experiments have been carried out on real traffic flow including a small city (Odense, Denmark) and a very big city (Beijing). The results reveal that the sequential version (RNN-LF) is capable of dealing effectively with traffic of small cities. They also confirm the scalability of GRNN-LF compared to the most competitive GPU-based software tools when dealing with big traffic flow such as Beijing urban data. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | Learning long-term flows | en_US |
dc.subject | Recurrent neural network | en_US |
dc.subject | Weather information | en_US |
dc.subject | Contextual information | en_US |
dc.title | A recurrent neural network for urban long-term traffic flow forecasting | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © The Author(s) 2020 | en_US |
dc.source.pagenumber | 3252-3265 | en_US |
dc.source.volume | 50 | en_US |
dc.source.journal | Applied intelligence (Boston) | en_US |
dc.identifier.doi | 10.1007/s10489-020-01716-1 | |
dc.identifier.cristin | 1820852 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |