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Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures

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dc.contributor.author Vlahogianni, EI en
dc.contributor.author Karlaftis, MG en
dc.date.accessioned 2014-03-01T02:08:32Z
dc.date.available 2014-03-01T02:08:32Z
dc.date.issued 2012 en
dc.identifier.issn 0924090X en
dc.identifier.uri http://hdl.handle.net/123456789/29657
dc.subject Complexity measures en
dc.subject Confidence bounds en
dc.subject Precipitation intensity en
dc.subject Recurrence quantification analysis en
dc.subject Traffic flow en
dc.subject Weather conditions en
dc.title Comparing traffic flow time-series under fine and adverse weather conditions using recurrence-based complexity measures en
heal.type journalArticle en
heal.identifier.primary 10.1007/s11071-012-0399-x en
heal.identifier.secondary http://dx.doi.org/10.1007/s11071-012-0399-x en
heal.publicationDate 2012 en
heal.abstract Adverse weather conditions may have a significant impact on the statistical characteristics of traffic variables such as volume and speed, and consequently on their predictability.We address the problem of freeway lane speed variability under the effect of precipitation episodes and present a non-linear dynamics methodology based on recurrence quantification analysis to compare speed evolution under fine and adverse weather conditions. Findings indicate that, under fine weather conditions, section travel speed is an adequate descriptor of traffic evolution in the left and middle lanes for high demand, but fails in cases of incidents or low traffic. Moreover, when it rains, regardless of intensity and duration, a significantly dissimilar evolution of lane speed is observed; this difference should be considered in the process of short-term traffic forecasting. © Springer Science+Business Media B.V. 2012. en
heal.journalName Nonlinear Dynamics en
dc.identifier.doi 10.1007/s11071-012-0399-x en
dc.identifier.volume 69 en
dc.identifier.issue 4 en
dc.identifier.spage 1949 en
dc.identifier.epage 1963 en


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