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dc.contributor.authorAbdullatif, Amr R.A.
dc.contributor.authorMasulli, F.
dc.contributor.authorRovetta, S.
dc.date.accessioned2020-01-20T10:05:45Z
dc.date.accessioned2020-01-23T10:09:30Z
dc.date.available2020-01-20T10:05:45Z
dc.date.available2020-01-23T10:09:30Z
dc.date.issued2017-09
dc.identifier.citationAbdullatif A, Masulli F and Rovetta S (2017) Tracking time evolving data stream for short-term traffic forecasting. Data Science and Engineering. 2: 210-223.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17598
dc.descriptionYesen_US
dc.description.abstractData streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/s41019-017-0048-yen_US
dc.rights©The Author(s) 2017. This article is an open access publication. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
dc.subjectTraffic forecastingen_US
dc.subjectFuzzy clusteringen_US
dc.subjectBig dataen_US
dc.subjectEnsemble modelen_US
dc.subjectEvolving data streamsen_US
dc.titleTracking time evolving data streams for short-term traffic forecastingen_US
dc.status.refereedYesen_US
dc.date.Accepted2017-10-02
dc.date.application2017-10-24
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.date.updated2020-01-20T10:05:47Z
refterms.dateFOA2020-01-23T10:10:05Z


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