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dc.contributor.advisorMoshe E. Ben-Akiva.en_US
dc.contributor.authorChen, Siyu,S.M.Massachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2019-12-13T18:53:27Z
dc.date.available2019-12-13T18:53:27Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123233
dc.descriptionThesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 79-83).en_US
dc.description.abstractThis thesis addresses the problem of calibrating activity-based travel demand model systems. After estimation, it is common practice to use aggregate measurements to calibrate the estimated model system's parameters. However, calibration of activity-based model systems has received much less attention. Existing calibration approaches are myopic heuristics in the sense that they do not consider inter-dependency among choice-models and do not have a systematic way to adjust model parameters. Also, other simulation-based approaches do not perform well in large-scale applications. In this thesis, we focus on utility-maximizing nested logit activity-based model systems and calibrating count based aggregate statistics like OD flows, mode shares, activity shares and so on. We formulate the calibration problem as a simulation-based optimization problem and propose a stochastic gradient-based solution procedure to solve it. The solution procedure relies on microsimulation to calculate expected aggregate statistics of interest to the calibration problem. Additionally, we derive approximate analytical expressions for the gradient of the objective function -that are evaluated through microsimulation on mini-batches of the population. The proposed solution procedure is sensitive to the fundamental structure of the activity-based model system and is non-myopic in considering the dependencies across its model components. Finally, we show -through a real-world application- that the proposed solution procedure outperforms other state-of-the-art purely simulation-based optimization approaches in terms of computational efficiency, stability, and convergence. We also compare various gradient-based solution algorithms to determine the best algorithm to update the parameters. This work has the potential to facilitate wider and easier application of activity-based model systems.en_US
dc.description.statementofresponsibilityby Siyu Chen.en_US
dc.format.extent83 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleCalibrating activity-based travel demand model system via microsimulationen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Transportationen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineeringen_US
dc.identifier.oclc1129597143en_US
dc.description.collectionS.M.inTransportation Massachusetts Institute of Technology, Department of Civil and Environmental Engineeringen_US
dspace.imported2019-12-13T18:53:25Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentCivEngen_US


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