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Short-term forecasting of intermodal freight using ANNs and SVR: Case of the Port of Algeciras Bay

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Short-term forecasting of intermodal freight using ANNs and SVR: Case of the Port of Algeciras Bay

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dc.contributor.author Moscoso López, Jose-Antonio es_ES
dc.contributor.author Turias, I.J. es_ES
dc.contributor.author Jimenez Come, M.J. es_ES
dc.contributor.author Ruiz-Aguilar, J.J. es_ES
dc.contributor.author Cerban, M. es_ES
dc.coverage.spatial east=-5.437987651428557; north=36.131170374804796; name= Algeciras, Cádiz, Espanya
dc.date.accessioned 2017-11-06T07:37:06Z
dc.date.available 2017-11-06T07:37:06Z
dc.date.issued 2016-06-01
dc.identifier.isbn 9788460899600
dc.identifier.uri http://hdl.handle.net/10251/90474
dc.description.abstract [EN] Forecasting of future intermodal traffic demand is very important for decision making in ports operations management. The use of accurate prediction tools is an issue that awakens a lot of interest among transport researchers. Intermodal freight forecasting plays an important role in ports management and in the planning of the principal port activities. Hence, the study is carried out under the motivation of knowing that modeling the freight transport flows could facilitate the management of the infrastructure and optimize the resources of the ports facilities. The use of advanced models for freight forecasting is essential to improve the port level-service and competitiveness. In this paper, two forecasting-models are presented and compared to predict the freight volume. The models developed and tested are based on Artificial Neural Networks and Support Vector Machines. Both techniques are based in a historical data and these methods forecast the daily weight of the freight with one week in advance. The performance of the models is evaluated on real data from Ro-Ro freight transport in the Port of Algeciras Bay. This work proposes and compares different approaches to determine the best prediction. In order to select the best model a multicomparison procedure is developed using several statistical test. The results of the assessed models show a promising tool to predict Ro-Ro transport flows with accuracy. es_ES
dc.format.extent 9 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España) es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Logistic es_ES
dc.subject Modelling es_ES
dc.subject ANNNs es_ES
dc.subject SVM es_ES
dc.subject Port Planning es_ES
dc.title Short-term forecasting of intermodal freight using ANNs and SVR: Case of the Port of Algeciras Bay es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CIT2016.2015.3464
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Moscoso López, J.; Turias, I.; Jimenez Come, M.; Ruiz-Aguilar, J.; Cerban, M. (2016). Short-term forecasting of intermodal freight using ANNs and SVR: Case of the Port of Algeciras Bay. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1501-1509. https://doi.org/10.4995/CIT2016.2015.3464 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CIT2016. Congreso de Ingeniería del Transporte es_ES
dc.relation.conferencedate June 07-09,2016 es_ES
dc.relation.conferenceplace Valencia, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CIT/CIT2016/paper/view/3464 es_ES
dc.description.upvformatpinicio 1501 es_ES
dc.description.upvformatpfin 1509 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\3464 es_ES


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