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Off-line calibration of Dynamic Traffic Assignment models

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dc.contributor.advisor Moshe E. Ben-Akiva and Haris N. Koutsopoulos. en_US
dc.contributor.author Balakrishna, Ramachandran, 1978- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.date.accessioned 2008-01-10T17:25:00Z
dc.date.available 2008-01-10T17:25:00Z
dc.date.copyright 2006 en_US
dc.date.issued 2006 en_US
dc.identifier.uri http://dspace.mit.edu/handle/1721.1/35120 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/35120
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006. en_US
dc.description Includes bibliographical references (p. 197-212). en_US
dc.description.abstract Advances in Intelligent Transportation Systems (ITS) have resulted in the deployment of surveillance systems that automatically collect and store extensive network-wide traffic data. Dynamic Traffic Assignment (DTA) models have also been developed for a variety of dynamic traffic management applications. Such models are designed to estimate and predict the evolution of congestion through detailed models and algorithms that capture travel demand, network supply and their complex interactions. The availability of rich time-varying traffic data spanning multiple days thus provides the opportunity to calibrate a DTA model's many inputs and parameters, so that its outputs reflect field conditions. The current state of the art of DTA model calibration is a sequential approach, in which supply model calibration (assuming known demand inputs) is followed by demand calibration with fixed supply parameters. In this thesis, we develop an off-line DTA model calibration methodology for the simultaneous estimation of all demand and supply inputs and parameters, using sensor data. We adopt a minimization formulation that can use any general traffic data, and present approaches to solve the complex, non-linear, stochastic optimization problem. en_US
dc.description.abstract (cont.) Case studies with DynaMIT, a DTA model with traffic estimation and prediction capabilities, are used to demonstrate and validate the proposed methodology. A synthetic traffic network with known demand parameters and simulated sensor data is used to illustrate the improvement over the sequential approach, the ability to accurately recover underlying model parameters, and robustness in a variety of demand and supply situations. Archived sensor data and a network from Los Angeles, CA are then used to demonstrate scalability. The benefit of the proposed methodology is validated through a real-time test of the calibrated DynaMIT's estimation and prediction accuracy, based on sensor data not used for calibration. Results indicate that the simultaneous approach significantly outperforms the sequential state of the art. en_US
dc.description.statementofresponsibility by Ramachandran Balakrishna. en_US
dc.format.extent 212 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/35120 en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Civil and Environmental Engineering. en_US
dc.title Off-line calibration of Dynamic Traffic Assignment models en_US
dc.title.alternative Off-line calibration of DTA models en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. en_US
dc.identifier.oclc 71662913 en_US


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