New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data

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dc.contributor.authorManibardo, Eric L.
dc.contributor.authorLana, Ibai
dc.contributor.authorLobo, Jesus L.
dc.contributor.authorDel Ser, Javier
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:57:23Z
dc.date.available2024-07-24T11:57:23Z
dc.date.issued2020-07
dc.descriptionPublisher Copyright: © 2020 IEEE.
dc.description.abstractThis work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predicted may evolve over time (concept drift). Consequently, predictive models aimed to learn this pattern may become eventually obsolete, hence failing to sustain performance levels of practical use. To overcome this model degradation, online learning methods incrementally learn from new data samples arriving over time, and accommodate eventual changes along the data stream by implementing assorted concept drift strategies. In this manuscript we elaborate on the suitability of online learning methods to predict the road congestion level based on traffic speed time series data. We draw interesting insights on the performance degradation when the forecasting horizon is increased. As opposed to what is done in most literature, we provide evidence of the importance of assessing the distribution of classes over time before designing and tuning the learning model. This previous exercise may give a hint of the predictability of the different congestion levels under target. Experimental results are discussed over real traffic speed data captured by inductive loops deployed over Seattle (USA). Several online learning methods are analyzed, from traditional incremental learning algorithms to more elaborated deep learning models. As shown by the reported results, when increasing the prediction horizon, the performance of all models degrade severely due to the distribution of classes along time, which supports our claim about the importance of analyzing this distribution prior to the design of the model.en
dc.description.sponsorshipThe authors would like to thank the Basque Government for its funding support through the EMAITEK and ELKARTEK programs. Eric L. Manibardo receives funding support from the Basque Government through its BIKAINTEK PhD support program (grant no. 48AFW22019-00002).
dc.description.statusPeer reviewed
dc.identifier.citationManibardo , E L , Lana , I , Lobo , J L & Del Ser , J 2020 , New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data . in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings . , 9207661 , Proceedings of the International Joint Conference on Neural Networks , Institute of Electrical and Electronics Engineers Inc. , 2020 International Joint Conference on Neural Networks, IJCNN 2020 , Virtual, Glasgow , United Kingdom , 19/07/20 . https://doi.org/10.1109/IJCNN48605.2020.9207661
dc.identifier.citationconference
dc.identifier.doi10.1109/IJCNN48605.2020.9207661
dc.identifier.isbn9781728169262
dc.identifier.urihttps://hdl.handle.net/11556/2726
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85093860017&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
dc.relation.ispartofseriesProceedings of the International Joint Conference on Neural Networks
dc.relation.projectIDEusko Jaurlaritza, 48AFW22019-00002
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsconcept drift
dc.subject.keywordscongestion prediction
dc.subject.keywordsdeep learning
dc.subject.keywordsonline learning
dc.subject.keywordsTime series
dc.subject.keywordstraffic forecasting
dc.subject.keywordsSoftware
dc.subject.keywordsArtificial Intelligence
dc.titleNew Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Dataen
dc.typeconference output
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