Aggregation bias in traffic flow time series: The effects of ignoring it

Vlahogianni, EI; Karlaftis, MG

dc.contributor.author	Vlahogianni, EI	en
dc.contributor.author	Karlaftis, MG	en
dc.date.accessioned	2014-03-01T02:51:56Z
dc.date.available	2014-03-01T02:51:56Z
dc.date.issued	2009	en
dc.identifier.issn	14746670	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35763
dc.subject	Information analysis	en
dc.subject	Long-term memory	en
dc.subject	Road traffic	en
dc.subject	Stationarity	en
dc.subject	Statistical analysis	en
dc.subject.other	Data aggregation	en
dc.subject.other	Data resolutions	en
dc.subject.other	Data series	en
dc.subject.other	Fractional dynamics	en
dc.subject.other	Intelligent traffic management	en
dc.subject.other	Long memories	en
dc.subject.other	Long term memory	en
dc.subject.other	Road traffic	en
dc.subject.other	Spurious oscillations	en
dc.subject.other	Stationarity	en
dc.subject.other	Traffic flow	en
dc.subject.other	Traffic volumes	en
dc.subject.other	Traveler information systems	en
dc.subject.other	Data flow analysis	en
dc.subject.other	Highway traffic control	en
dc.subject.other	Time series	en
dc.subject.other	Time series analysis	en
dc.subject.other	Traffic surveys	en
dc.subject.other	Information management	en
dc.title	Aggregation bias in traffic flow time series: The effects of ignoring it	en
heal.type	conferenceItem	en
heal.identifier.primary	10.3182/20090902-3-US-2007.0041	en
heal.identifier.secondary	http://dx.doi.org/10.3182/20090902-3-US-2007.0041	en
heal.publicationDate	2009	en
heal.abstract	Current ITS infrastructure provides the ability to simultaneously capture and monitor a variety of transportation data in very fine intervals. However, in order for the data to be analyzed and integrated to the different intelligent traffic management and traveler information systems, the optimum data resolution should be selected. In many transportation studies it is suggested that data should be aggregated in order to avoid the effects of noise and spurious oscillations. In the present paper, we analyze the effect of data aggregation on the fractional dynamics of traffic volume and occupancy, as well as the information degradation between the original and the aggregated data. Results indicate that aggregation may suppress the long memory characteristics of traffic flow. Moreover, uncertainty associated with the aggregated data with respect to the original data series increases with coarser data resolutions. © 2009 IFAC.	en
heal.journalName	IFAC Proceedings Volumes (IFAC-PapersOnline)	en
dc.identifier.doi	10.3182/20090902-3-US-2007.0041	en
dc.identifier.spage	462	en
dc.identifier.epage	466	en