000124075 001__ 124075
000124075 005__ 20230706131808.0
000124075 0247_ $$2doi$$a10.1016/j.procs.2022.09.110
000124075 0248_ $$2sideral$$a132640
000124075 037__ $$aART-2022-132640
000124075 041__ $$aeng
000124075 100__ $$aVillarroya, Cristian
000124075 245__ $$aNeural Network-based Model for traffic prediction in the city of Valencia
000124075 260__ $$c2022
000124075 5060_ $$aAccess copy available to the general public$$fUnrestricted
000124075 5203_ $$aThere are many models that attempt to predict vehicular speed in urban and interurban roads, the noise pollution caused by traffic in cities, or even the traffic flow based on historical data from cameras or from people's mobile phones. Such information can be useful for administration authorities, and for researchers attempting to improve the living conditions of citizens. In this context, the aim of the present study is to design a model capable of predicting the traffic flow in the city of Valencia, Spain, based on data collected by electromagnetic loops distributed throughout the city. With a good traffic prediction, it will be possible to foresee possible traffic jams, and also to trigger countermeasures to mitigate them. Therefore, two models based on two recurrent neural networks of Long Short-Term Memory (LSTM) type have been designed to predict the traffic flow in the different streets of Valencia at the different hours of the day. We also study the influence of the specific characteristics used on the accuracy of the model. The results of our experiments show that, despite the high heterogeneity in terms of per-street traffic behaviour, it is possible to reach useful prediction models with low errors.
000124075 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096384-B-100
000124075 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000124075 592__ $$a0.507$$b2022
000124075 593__ $$aComputer Science (miscellaneous)$$c2022
000124075 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000124075 700__ $$aCalafate, Carlos T.
000124075 700__ $$aOnaindia, Eva
000124075 700__ $$aCano, Juan Carlos
000124075 700__ $$0(orcid)0000-0001-6945-7330$$aMartínez, Francisco J.$$uUniversidad de Zaragoza
000124075 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput.
000124075 773__ $$g207 (2022), 552-562$$tProcedia computer science$$x1877-0509
000124075 8564_ $$s2014525$$uhttps://zaguan.unizar.es/record/124075/files/texto_completo.pdf$$yVersión publicada
000124075 8564_ $$s2034425$$uhttps://zaguan.unizar.es/record/124075/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000124075 909CO $$ooai:zaguan.unizar.es:124075$$particulos$$pdriver
000124075 951__ $$a2023-07-06-12:25:46
000124075 980__ $$aARTICLE