{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/80943"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/80943","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Data-Driven Transit System Modeling Using Automated Fare Collection Data","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Wu, Laiyun; 0000-0001-5302-3888"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Kang, Jee Eun","Industrial and Systems Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10-29T16:48:22Z","date_published":"2019-10-29T16:48:22Z","updated_at":"2026-07-27T19:05:28Z","subjects":["transportation"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/80943","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kang, Jee Eun","Industrial and Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Wu, Laiyun; 0000-0001-5302-3888"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-10-29T16:48:22Z","2019","2019-08-09 13:39:56"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["transportation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/80943"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Automated Fare Collection (AFC) systems, often called smart transit card systems, have found use in public transportation systems worldwide. Not only do AFC systems enable a secure and fast way of fare collection, but also, they offer a cost-effective way of collecting and monitoring travel information of each user, recorded as time-stamped transactions. These data provide detailed travel information about transit system users that can potentially be informative for operators and planners for understanding traffic condition and travelers travel patterns, constructing models to find out travelers true ODs and optimize the transit route offerings. This dissertation is composed of four pieces related to the modeling and understanding of transit networks using AFC data. The first piece relates to methods for obtaining system level transit information from AFC data. Monitoring transit system \"health\" by extracting and tracking such quantities as travel time, transfer time, number of passengers, etc., is critical to the benefit of travelers, planners and operators within a transit system. This chapter presents methods for obtaining system level transit information from AFC system, which provides hour-to-hour, day-to-day transit information. The AFC data of public transit system in Seoul, South Korea is used as an example to illustrate the proposed data extraction methods and analysis, to further provide both methodological and practical guidance for researchers and data-handling analysts.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-Driven Transit System Modeling Using Automated Fare Collection Data"]}]}],"canonical_facts":{"dc:contributor":["Kang, Jee Eun","Industrial and Systems Engineering"],"dc:creator":["Wu, Laiyun; 0000-0001-5302-3888"],"dc:date":["2019-10-29T16:48:22Z","2019","2019-08-09 13:39:56"],"dc:description":["Ph.D.","Automated Fare Collection (AFC) systems, often called smart transit card systems, have found use in public transportation systems worldwide. Not only do AFC systems enable a secure and fast way of fare collection, but also, they offer a cost-effective way of collecting and monitoring travel information of each user, recorded as time-stamped transactions. These data provide detailed travel information about transit system users that can potentially be informative for operators and planners for understanding traffic condition and travelers travel patterns, constructing models to find out travelers true ODs and optimize the transit route offerings. This dissertation is composed of four pieces related to the modeling and understanding of transit networks using AFC data. The first piece relates to methods for obtaining system level transit information from AFC data. Monitoring transit system \"health\" by extracting and tracking such quantities as travel time, transfer time, number of passengers, etc., is critical to the benefit of travelers, planners and operators within a transit system. This chapter presents methods for obtaining system level transit information from AFC system, which provides hour-to-hour, day-to-day transit information. The AFC data of public transit system in Seoul, South Korea is used as an example to illustrate the proposed data extraction methods and analysis, to further provide both methodological and practical guidance for researchers and data-handling analysts.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/80943"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["transportation"],"dc:title":["Data-Driven Transit System Modeling Using Automated Fare Collection Data"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:28Z"}