Calibration of dynamic traffic assignment models with point-to-point traffic surveillance

Vaze, V; Antoniou, C; Wen, Y; Ben-Akiva, M

dc.contributor.author	Vaze, V	en
dc.contributor.author	Antoniou, C	en
dc.contributor.author	Wen, Y	en
dc.contributor.author	Ben-Akiva, M	en
dc.date.accessioned	2014-03-01T01:29:57Z
dc.date.available	2014-03-01T01:29:57Z
dc.date.issued	2009	en
dc.identifier.issn	0361-1981	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19415
dc.subject	Dynamic Traffic Assignment	en
dc.subject	Traffic Surveillance	en
dc.subject	Point To Point	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Transportation	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Automatic vehicle identification	en
dc.subject.other	Calibration accuracy	en
dc.subject.other	Calibration problems	en
dc.subject.other	Demand and supply	en
dc.subject.other	Dynamic traffic assignments	en
dc.subject.other	Estimation results	en
dc.subject.other	Gradient approximation	en
dc.subject.other	Joint calibration	en
dc.subject.other	Metaheuristic	en
dc.subject.other	Model parameters	en
dc.subject.other	New York	en
dc.subject.other	Path search	en
dc.subject.other	Point sensors	en
dc.subject.other	Random searches	en
dc.subject.other	Real traffic	en
dc.subject.other	Sensing technology	en
dc.subject.other	Stochastic optimizations	en
dc.subject.other	Synthetic study	en
dc.subject.other	Traffic data	en
dc.subject.other	Traffic management	en
dc.subject.other	Traffic simulators	en
dc.subject.other	Traffic surveillance	en
dc.subject.other	Travel information	en
dc.subject.other	Travel time measurements	en
dc.subject.other	Approximation algorithms	en
dc.subject.other	Calibration	en
dc.subject.other	Competition	en
dc.subject.other	Highway traffic control	en
dc.subject.other	Time measurement	en
dc.subject.other	Vehicle actuated signals	en
dc.subject.other	Traffic surveys	en
dc.title	Calibration of dynamic traffic assignment models with point-to-point traffic surveillance	en
heal.type	journalArticle	en
heal.identifier.primary	10.3141/2090-01	en
heal.identifier.secondary	http://dx.doi.org/10.3141/2090-01	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Accurate 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
heal.publisher	NATL ACAD SCIENCES	en
heal.journalName	Transportation Research Record	en
dc.identifier.doi	10.3141/2090-01	en
dc.identifier.isi	ISI:000268737100001	en
dc.identifier.issue	2090	en
dc.identifier.spage	1	en
dc.identifier.epage	9	en