A comparative study on the performance of evolutionary fuzzy and crisp rule based classification methods in congestion prediction

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dc.contributor.authorOnieva Caracuel, Enrique
dc.contributor.authorLópez García, Pedro
dc.contributor.authorMasegosa Arredondo, Antonio David
dc.contributor.authorOsaba, Eneko
dc.contributor.authorPerallos Ruiz, Asier
dc.date.accessioned2026-03-27T10:48:57Z
dc.date.available2026-03-27T10:48:57Z
dc.date.issued2016
dc.date.updated2026-03-27T10:48:57Z
dc.descriptionPonencia presentada en la 6 th Transport Research Arena Conference. celebrada en Varsovia, Polonia, entre el 18 y el 21 de abril de 2016es
dc.description.abstractAccurate estimation of the future state of the traffic is an attracting area for researchers in the field of Intelligent Transportation Systems (ITS). This kind of predictions can lead to traffic managers and drivers to act in consequence, reducing the economic and social impact of a possible congestion. Due to the inter-urban traffic information nature, the task of predicting the future state of the traffic requires, in most cases, a non-linear patterns search in the input data. In recent years, a wide variety of models has been used to solve this problem in the most accurate way. Due to that, models generated to provide information about the future state of the road are, usually, incomprehensible to a human operator, making impossible to give him/her an explanation about the causes of the prediction. Given the capacity of rule based systems to explain the reasoning followed to classify a new pattern, the advantages and disadvantages of such approaches are explored in this work. To conduct such task, datasets recorded from the California Department of Transportation are created. A 9-kilometer section of the I5 highway of Sacramento is used for this research. Two different types of datasets are built for the experimentation. One of them contains the entire information recorded. The other one contains with a simplified version of the information, considering only the first, middle and last monitored points of the road. Twelve prediction horizons, from 5 to 60 minutes, were considered for prediction. An experimental comparative study involving 16 state of the art techniques is performed. Techniques tested include those that fall within the categories of Evolutionary Crisp Rule Learning (ECRL) and Evolutionary Fuzzy Rule Learning (EFRL). These methods were selected since they offer to the final user, not only a prediction, but also a legible model about the way in which the decision was taken. Techniques are compared in terms of accuracy and complexity of the models generated.en
dc.description.sponsorshipThis work has been partially funded by the TIMON Project (Enhanced real time services for an optimized multimodal mobility relying on cooperative networks and open data). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 636220.en
dc.identifier.citationOnieva, Lopez-Garcia, Masegosa, Osaba, & Perallos. (2016). A comparative study on the performance of evolutionary fuzzy and crisp rule based classification methods in congestion prediction. Transportation Research Procedia, 14, 4458-4467. https://doi.org/10.1016/J.TRPRO.2016.05.368
dc.identifier.doi10.1016/J.TRPRO.2016.05.368
dc.identifier.eissn2352-1465
dc.identifier.issn2352-1457
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5571
dc.language.isoeng
dc.publisherElsevier B.V.
dc.rights© 2016 The Authors
dc.subject.otherEvolutionary crisp rule learning
dc.subject.otherEvolutionary fuzzy rule learning
dc.subject.otherFuzzy logic
dc.subject.otherGenetic algorithms
dc.subject.otherIntelligent transportation systems
dc.subject.otherTraffic congestion prediction
dc.subject.otherTraffic forecast
dc.titleA comparative study on the performance of evolutionary fuzzy and crisp rule based classification methods in congestion predictionen
dc.typeconference paper
dcterms.accessRightsopen access
oaire.citation.endPage4467
oaire.citation.startPage4458
oaire.citation.titleTransportation Research Procedia
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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