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https://hdl.handle.net/1822/67991
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Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Fernandes, B. | por |
dc.contributor.author | Silva, Fábio | por |
dc.contributor.author | Alaiz-Moretón, Hector | por |
dc.contributor.author | Novais, Paulo | por |
dc.contributor.author | Analide, Cesar | por |
dc.contributor.author | Neves, José | por |
dc.date.accessioned | 2020-11-03T16:51:45Z | - |
dc.date.issued | 2019-01 | - |
dc.identifier.citation | Fernandes, B., Silva, F., Alaiz-Moretón, H., et. al. (2019, April). Traffic Flow Forecasting on Data-Scarce Environments Using ARIMA and LSTM Networks. In World Conference on Information Systems and Technologies (pp. 273-282). Springer | por |
dc.identifier.isbn | 9783030161804 | por |
dc.identifier.issn | 2194-5357 | - |
dc.identifier.uri | https://hdl.handle.net/1822/67991 | - |
dc.description.abstract | Traffic flow forecasting has been in the mind of researchers for the last decades, remaining a challenge mainly due to its stochastic nonlinear nature. In fact, producing accurate traffic flow predictions would be extremely useful not only for drivers but also for those more vulnerable in the road, such as pedestrians or cyclists. With a citizen-first approach in mind, forecasting models can be used to help advise citizens based on the perception of outdoor risks, dangerous behaviors and time delays, among others. Hence, this work develops and evaluates the accuracy of different ARIMA and LSTM based-models for traffic flow forecasting on data-scarce and non-data-scarce environments. The obtained results show the great potential of LSTM networks while, in contrast, expose the poor performance of ARIMA models on large datasets. Nonetheless, both were able to identify trends and the cyclic nature of traffic. | por |
dc.description.sponsorship | COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013, being partially supported by a Portuguese doctoral grant, SFRH/BD/130125/2017, issued by FCT in Portugal | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | por |
dc.rights | restrictedAccess | por |
dc.subject | AutoRegressive Integrated Moving Average | por |
dc.subject | Data-scarce environments | por |
dc.subject | Long Short-Term Memory | por |
dc.subject | Road safety | por |
dc.subject | Traffic flow forecasting | por |
dc.title | Traffic flow forecasting on data-scarce environments using ARIMA and LSTM Networks | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-16181-1_26 | por |
oaire.citationStartPage | 273 | por |
oaire.citationEndPage | 282 | por |
oaire.citationConferencePlace | Budva, Montenegro | por |
oaire.citationVolume | 930 | por |
dc.date.updated | 2020-11-03T14:47:42Z | - |
dc.identifier.doi | 10.1007/978-3-030-16181-1_26 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
sdum.export.identifier | 7467 | - |
sdum.journal | Advances in Intelligent Systems and Computing | por |
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CR_Traffic_Predicition_WorldCist_BF.pdf Acesso restrito! | 771,68 kB | Adobe PDF | Ver/Abrir |