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Estimation of the automatic vehicle identification based spatial travel time information collected in Stockholm
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Traffic and Logistics (closed 20110301).
2010 (English)In: IET INTELL TRANSP SYST, ISSN 1751-956X, Vol. 4, no 4, 298-306 p.Article in journal (Refereed) Published
Abstract [en]

To support the implementation of real-time traffic information systems in the Stockholm city area using automatic vehicle identification (AVI) data, a preliminary travel time analysis tool has been developed. The program can manage and analyse travel time measurements in a distributed database server where both online and historical traffic information are saved. Meanwhile, several existing travel time estimation algorithms are implemented in the travel time analysis program, and are evaluated using four months of AVI data collected in the urban streets and arterials of and near the Stockholm downtown area. The advantages and disadvantages of those algorithms are also analysed using the highly noisy travel time measurements collected under the urban context. In addition, the authors have also evaluated a common statistical median filtering approach and suggested some modifications for AVI data estimation. In general, all these algorithms have the potential to be applied for real daily travel time estimation and the statistical median filter with modifications has been suggested for historical travel time estimation in real application. Finally, the authors point out an essential problem in travel time estimation and suggest a direction that may have the potential to improve the online traffic information quality.

Place, publisher, year, edition, pages
2010. Vol. 4, no 4, 298-306 p.
National Category
Civil Engineering Computer and Information Science
URN: urn:nbn:se:kth:diva-27372DOI: 10.1049/iet-its.2009.0149ISI: 000284307500009ScopusID: 2-s2.0-78751496658OAI: diva2:376971

QC 20150721

Available from: 2010-12-13 Created: 2010-12-13 Last updated: 2015-07-21Bibliographically approved

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