Road Segments Traffic Dependencies Study Using Cross-Correlation

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dc.contributor.authorBenabdallah Benarmas, Redouane
dc.contributor.authorBeghdad Bey, Kadda
dc.date.accessioned2022-04-14T12:02:03Z
dc.date.available2022-04-14T12:02:03Z
dc.date.issued2022-01-24
dc.descriptionForum Intervention of Artificial Intelligence and Its Applications. Faculty of Exat science. University of Eloueden_US
dc.description.abstractTraffic Prediction on a urban road network become more complex face to exponential growth in the volume of traffic, it is necessary to study the relationship between road segments before the prediction calculation. The spatial correlation theory has been well developed to interpret the dependency for understanding how time series are related in multivariate model. In large scale road network modeled by Multivariate Time Series, the Spatialtemporal dependencies detection can limit the use of only data collected from points related to a target point to be predicted. This paper present a Cross- Correlation as method to dependency analysis between traffic road segments, Scatterplot of Cross-Correlation is proposed to reveal the dependency, we provide a comparative analysis between a three correlation coefficients sush as Spearman, Kendal and Person to conclude the best one. To illustrate our study, the methodology is applied to the 6th road ring as the most crowded area of Beijing.en_US
dc.identifier.citationBenabdallah Benarmas, Redouane, Beghdad Bey, Kadda. Road Segments Traffic Dependencies Study Using Cross-Correlation. Forum of Artificial Intelligence and Its Applications. 24-26 Jan 2022. Faculty of Exat science. University of Eloued. [visited in ../../….]. available from [copy the link here]en_US
dc.identifier.urihttp://dspace.univ-eloued.dz/handle/123456789/10836
dc.language.isoenen_US
dc.publisherUniversity of Eloued جامعة الواديen_US
dc.subjectIntelligent Transportation System, Traffic Forecasting, Time series, Cross-Correlationen_US
dc.titleRoad Segments Traffic Dependencies Study Using Cross-Correlationen_US
dc.typeOtheren_US

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