{"id":{"repo_id":"cornell","oai_identifier":"oai:ecommons.cornell.edu:1813/34070"},"canonical_url":"https://search.dev.ndltd.org/etd/cornell/oai:ecommons.cornell.edu:1813/34070","repository":{"repo_id":"cornell","name":"Cornell University","base_url":"https://ecommons.cornell.edu/server/oai/request"},"display":{"title":"Multiclass Origin-Destination Estimation Using Multiple Data Types","abstract":"Estimating O-D tables for trucks is of substantial interest due to different emission characteristics, pavement damage, etc of trucks. This thesis proposes a bilevel optimization model and corresponding solution method for static multi-class O-D estimation using various types of data. Limited memory BFGS method with bounded constraints is used for solving the upper level optimization, which is used to derive O-D table entries by minimizing the sum of squared differences between observations from different data sources and the predictions of those values. A probit model is assumed in the lower-level stochastic user equilibrium problem for flow prediction. Extensive experiments have been performed on a test network with different types of link count sensors and turning movements. The tests verify the problem formulation and solution algorithm, and offer important insights into the multiclass O-D estimation process with different types of data available.","abstract_html":"Estimating O-D tables for trucks is of substantial interest due to different emission characteristics, pavement damage, etc of trucks. This thesis proposes a bilevel optimization model and corresponding solution method for static multi-class O-D estimation using various types of data. Limited memory BFGS method with bounded constraints is used for solving the upper level optimization, which is used to derive O-D table entries by minimizing the sum of squared differences between observations from different data sources and the predictions of those values. A probit model is assumed in the lower-level stochastic user equilibrium problem for flow prediction. Extensive experiments have been performed on a test network with different types of link count sensors and turning movements. The tests verify the problem formulation and solution algorithm, and offer important insights into the multiclass O-D estimation process with different types of data available.","abstract_has_math":false,"creators":["Zhao, Qing"],"institution":"Cornell University","degree_name":"M.S., Civil and Environmental Engineering","degree_level":"Master of Science","degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Gao, Huaizhu","Topaloglu, Huseyin"],"year":2013,"date_issued":"2013-05-26","date_published":"2013-05-26","updated_at":"2026-07-24T01:49:02Z","subjects":["OD estimation","Multiclass","Multiple data"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1813/34070","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Gao, Huaizhu","Topaloglu, Huseyin"]},{"key":"dc:creator","label":"Author","values":["Zhao, Qing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-09-05T15:57:03Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-05-27T06:00:31Z"]},{"key":"dc:date.issued","label":"Date","values":["2013-05-26"]},{"key":"dc:type","label":"Dc Type","values":["dissertation or thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil and Environmental Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master of Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S., Civil and Environmental Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Cornell University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["OD estimation","Multiclass","Multiple data"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1813/34070"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Estimating O-D tables for trucks is of substantial interest due to different emission characteristics, pavement damage, etc of trucks. This thesis proposes a bilevel optimization model and corresponding solution method for static multi-class O-D estimation using various types of data. Limited memory BFGS method with bounded constraints is used for solving the upper level optimization, which is used to derive O-D table entries by minimizing the sum of squared differences between observations from different data sources and the predictions of those values. A probit model is assumed in the lower-level stochastic user equilibrium problem for flow prediction. Extensive experiments have been performed on a test network with different types of link count sensors and turning movements. The tests verify the problem formulation and solution algorithm, and offer important insights into the multiclass O-D estimation process with different types of data available."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multiclass Origin-Destination Estimation Using Multiple Data Types"]}]}],"canonical_facts":{"dc:contributor.committeemember":["Gao, Huaizhu","Topaloglu, Huseyin"],"dc:creator":["Zhao, Qing"],"dc:date.accessioned":["2013-09-05T15:57:03Z"],"dc:date.available":["2018-05-27T06:00:31Z"],"dc:date.issued":["2013-05-26"],"dc:description.abstract":["Estimating O-D tables for trucks is of substantial interest due to different emission characteristics, pavement damage, etc of trucks. This thesis proposes a bilevel optimization model and corresponding solution method for static multi-class O-D estimation using various types of data. Limited memory BFGS method with bounded constraints is used for solving the upper level optimization, which is used to derive O-D table entries by minimizing the sum of squared differences between observations from different data sources and the predictions of those values. A probit model is assumed in the lower-level stochastic user equilibrium problem for flow prediction. Extensive experiments have been performed on a test network with different types of link count sensors and turning movements. The tests verify the problem formulation and solution algorithm, and offer important insights into the multiclass O-D estimation process with different types of data available."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/1813/34070"],"dc:language.iso":["en_US"],"dc:subject":["OD estimation","Multiclass","Multiple data"],"dc:title":["Multiclass Origin-Destination Estimation Using Multiple Data Types"],"dc:type":["dissertation or thesis"],"thesis:degree_discipline":["Civil and Environmental Engineering"],"thesis:degree_level":["Master of Science"],"thesis:degree_name":["M.S., Civil and Environmental Engineering"],"thesis:institution_name":["Cornell University"]},"updated_at":"2026-07-24T01:49:02Z"}