Bayesian Calibration for Logit Model Microsimulations: Case for PECAS SD in San Diego

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atmire.migration.oldid5844
dc.contributor.advisorHunt, John Douglas
dc.contributor.authorHill, Graham
dc.contributor.committeememberKattan, Lina
dc.contributor.committeememberDann, Markus
dc.contributor.committeememberWang, Xin
dc.date.accessioned2017-08-10T15:15:17Z
dc.date.available2017-08-10T15:15:17Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractBayesian inference is a versatile method for incorporating new information into a model while still respecting existing knowledge. One application of Bayesian inference is the calibration of models that are controlled by a large number of parameters, but where the data usable for calibration is incomplete or unreliable. Microsimulation models of urban development fit both of these criteria, but calibrating them is further complicated by their non-determinism. I investigated a calibration method called Bayesian Expected Value Calibration, which is designed to overcome non-determinism while incorporating existing knowledge, in the context of the PECAS Space Development model of the San Diego area. The test consisted of creating synthetic data using known behavioural parameters, calibrating the Space Development model to targets derived from the synthetic data and with priors reflecting imperfect existing knowledge, and assessing how closely the calibrated parameters matched the true values. I found that BEVC was generally effective at converging towards the true values of the parameters, and often received meaningful contributions from both the prior knowledge and the new observations under a range of plausible conditions. As would be expected from Bayesian theory, increasing the number of observations or the amount of useful prior knowledge improved the accuracy of the calibration. The method was robust under reasonable levels of human fallibility in creating the priors, and only suffered from significant loss of accuracy under extreme assumptions. However, more sophisticated methods of objectively determining the weights to assign to the data sources did not significantly improve calibration accuracy.en_US
dc.identifier.citationHill, G. (2017). Bayesian Calibration for Logit Model Microsimulations: Case for PECAS SD in San Diego (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25096en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/25096
dc.identifier.urihttp://hdl.handle.net/11023/4007
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectUrban and Regional Planning
dc.subjectStatistics
dc.subjectEngineering--Civil
dc.subject.otherLand use models
dc.subject.otherMicrosimulation
dc.subject.otherBayesian inference
dc.subject.otherModel calibration
dc.titleBayesian Calibration for Logit Model Microsimulations: Case for PECAS SD in San Diego
dc.typemaster thesis
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
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