{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99117"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99117","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Stochastic numerical approximation approaches for estimation of traffic volume under travel demand uncertainties","abstract":"The traditional deterministic process of trip assignment does not account for uncertainties in traffic demands. These point-estimate based solutions often results in large differences between forecasted and actual traffic volumes thereby imposing huge financial burdens upon development agencies. In this work, stochastic treatment has been given to the trip assignment problem, specifically the network user equilibrium problem solved using the variational inequality method, under demand uncertainties modeled as random inputs. Smolyak sparse grid interpolation technique was successfully applied to the problem and compared to Monte Carlo sampling. Performance of constructed interpolant was evaluated through output distribution recovery , statistical moment estimation, and computation time comparisons. Ability of sparse grid to efficiently handle demand uncertainties using as many as 5 times fewer points than Monte Carlo sampling in pragmatically sized transportation networks was demonstrated.","abstract_html":"The traditional deterministic process of trip assignment does not account for uncertainties in traffic demands. These point-estimate based solutions often results in large differences between forecasted and actual traffic volumes thereby imposing huge financial burdens upon development agencies. In this work, stochastic treatment has been given to the trip assignment problem, specifically the network user equilibrium problem solved using the variational inequality method, under demand uncertainties modeled as random inputs. Smolyak sparse grid interpolation technique was successfully applied to the problem and compared to Monte Carlo sampling. Performance of constructed interpolant was evaluated through output distribution recovery , statistical moment estimation, and computation time comparisons. Ability of sparse grid to efficiently handle demand uncertainties using as many as 5 times fewer points than Monte Carlo sampling in pragmatically sized transportation networks was demonstrated.","abstract_has_math":false,"creators":["Shukla, Kumar Neelotpal"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Meidani, Hadi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-02T19:59:44Z","date_published":"2018-03-02T19:59:44Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Trip assignment","User equilibrium","Variational inequality","Smolyak sparse grid","Polynomial chaos expansions"],"languages":["en"],"rights":["Copyright 2017 Kumar Neelotpal Shukla"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99117","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meidani, Hadi"]},{"key":"dc:creator","label":"Author","values":["Shukla, Kumar Neelotpal"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-02T19:59:44Z","2020-03-03T10:15:22Z","2017-07-19","2017-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Trip assignment","User equilibrium","Variational inequality","Smolyak sparse grid","Polynomial chaos expansions"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Kumar Neelotpal Shukla"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99117"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The traditional deterministic process of trip assignment does not account for uncertainties in traffic demands. 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Ability of sparse grid to efficiently handle demand uncertainties using as many as 5 times fewer points than Monte Carlo sampling in pragmatically sized transportation networks was demonstrated.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-08-01","The student, Kumar Neelotpal Shukla, accepted the attached license on 2017-07-18 at 10:41.","The student, Kumar Neelotpal Shukla, submitted this Thesis for approval on 2017-07-18 at 12:01.","This Thesis was approved for publication on 2017-07-19 at 08:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11510 on 2018-03-02 at 13:02:29","Made available in DSpace on 2018-03-02T19:59:44Z (GMT). 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These point-estimate based solutions often results in large differences between forecasted and actual traffic volumes thereby imposing huge financial burdens upon development agencies. In this work, stochastic treatment has been given to the trip assignment problem, specifically the network user equilibrium problem solved using the variational inequality method, under demand uncertainties modeled as random inputs. Smolyak sparse grid interpolation technique was successfully applied to the problem and compared to Monte Carlo sampling. Performance of constructed interpolant was evaluated through output distribution recovery , statistical moment estimation, and computation time comparisons. 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