Show simple item record

dc.contributor.authorVaze, Vikrant
dc.contributor.authorAntoniou, Constantinos
dc.contributor.authorWen, Yang
dc.contributor.authorBen-Akiva, Moshe E.
dc.date.accessioned2014-08-26T15:42:37Z
dc.date.available2014-08-26T15:42:37Z
dc.date.issued2009-07
dc.identifier.issn0361-1981
dc.identifier.urihttp://hdl.handle.net/1721.1/89062
dc.description.abstractAccurate calibration of demand and supply simulators within a dynamic traffic assignment system is critical for consistent travel information and efficient traffic management. Emerging traffic surveillance devices such as automatic vehicle identification (AVI) technology provide a rich source of disaggregated traffic data. A methodology for the joint calibration of demand and supply model parameters using travel time measurements obtained from these emerging traffic-sensing technologies is presented. The calibration problem has been formulated as a stochastic optimization framework. Two different algorithms are used for solving the calibration problem: a gradient approximation-based path search method and a random search metaheuristic. The methodology is first tested by using a small synthetic study network to illustrate its effectiveness and obtain insight into its operation. The methodology is further applied to a real traffic network in Lower Westchester County, New York, to demonstrate its scalability. The estimation results are tested by using a calibrated microscopic traffic simulator. The results are compared with the base case of calibration by the use of only the conventional point sensor data. The results indicate that use of AVI data significantly improves calibration accuracy.en_US
dc.language.isoen_US
dc.publisherTransportation Research Board of the National Academiesen_US
dc.relation.isversionofhttp://dx.doi.org/10.3141/2090-01en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceTransportation Research Recorden_US
dc.titleCalibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillanceen_US
dc.typeArticleen_US
dc.identifier.citationVaze, Vikrant, Constantinos Antoniou, Yang Wen, and Moshe Ben-Akiva. “Calibration of Dynamic Traffic Assignment Models with Point-to-Point Traffic Surveillance.” Transportation Research Record: Journal of the Transportation Research Board 2090, no. 1 (July 30, 2009): 1–9.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.contributor.mitauthorVaze, Vikranten_US
dc.contributor.mitauthorWen, Yangen_US
dc.contributor.mitauthorBen-Akiva, Moshe E.en_US
dc.relation.journalTransportation Research Record: Journal of the Transportation Research Boarden_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsVaze, Vikrant; Antoniou, Constantinos; Wen, Yang; Ben-Akiva, Mosheen_US
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record