{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/140174"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/140174","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Walking to transit – using big data to analyze bus and train ridership in Los Angeles","abstract":"Los Angeles passed one of the largest sales taxes in the country in 2016, which will give the county unprecedented financing in improving public transportation. Public transit ridership has been declining despite hefty investments, and it is important to understand why transit has not picked up. Studying current pedestrian-induced ridership is crucial as walkability is key in affecting ridership. Many prior studies assume linear relationships with established variables or explore transformed variables which have constrained assumptions. Machine learning models have the potential to discover nonlinear relationships such as step function and curvilinear relationships, which will help planners and policy makers make effective development decisions.","abstract_html":"Los Angeles passed one of the largest sales taxes in the country in 2016, which will give the county unprecedented financing in improving public transportation. Public transit ridership has been declining despite hefty investments, and it is important to understand why transit has not picked up. Studying current pedestrian-induced ridership is crucial as walkability is key in affecting ridership. Many prior studies assume linear relationships with established variables or explore transformed variables which have constrained assumptions. Machine learning models have the potential to discover nonlinear relationships such as step function and curvilinear relationships, which will help planners and policy makers make effective development decisions.","abstract_has_math":false,"creators":["Klo’e, Ng Yim Chew"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Urban Studies and Planning","school":null,"contributors":[],"advisors":["Sevstuk, Professor Andres"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09","date_published":"2021-09","updated_at":"2026-07-22T22:20:55Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/140174","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sevstuk, Professor Andres"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Urban Studies and Planning"]},{"key":"dc:creator","label":"Author","values":["Klo’e, Ng Yim Chew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-02-07T15:28:29Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-02-07T15:28:29Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Urban Studies and Planning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/140174"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Los Angeles passed one of the largest sales taxes in the country in 2016, which will give the county unprecedented financing in improving public transportation. Public transit ridership has been declining despite hefty investments, and it is important to understand why transit has not picked up. Studying current pedestrian-induced ridership is crucial as walkability is key in affecting ridership. Many prior studies assume linear relationships with established variables or explore transformed variables which have constrained assumptions. Machine learning models have the potential to discover nonlinear relationships such as step function and curvilinear relationships, which will help planners and policy makers make effective development decisions."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Walking to transit – using big data to analyze bus and train ridership in Los Angeles"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sevstuk, Professor Andres"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Urban Studies and Planning"],"dc:creator":["Klo’e, Ng Yim Chew"],"dc:date.accessioned":["2022-02-07T15:28:29Z"],"dc:date.available":["2022-02-07T15:28:29Z"],"dc:date.issued":["2021-09"],"dc:description.abstract":["Los Angeles passed one of the largest sales taxes in the country in 2016, which will give the county unprecedented financing in improving public transportation. Public transit ridership has been declining despite hefty investments, and it is important to understand why transit has not picked up. Studying current pedestrian-induced ridership is crucial as walkability is key in affecting ridership. Many prior studies assume linear relationships with established variables or explore transformed variables which have constrained assumptions. Machine learning models have the potential to discover nonlinear relationships such as step function and curvilinear relationships, which will help planners and policy makers make effective development decisions."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/140174"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Walking to transit – using big data to analyze bus and train ridership in Los Angeles"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Urban Studies and Planning"]},"updated_at":"2026-07-22T22:20:55Z"}