{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81810"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81810","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining","abstract":"Mining traffic anomalies. Identification and characterization of traffic anomalies on massive road networks is a vital component of traffic monitoring [44]. Anomaly identification can be used to reduce congestion, increase safety, and provide transportation engineers with better information for traffic forecasting and road network design. However, due to the size, complexity and dynamics of such transportation networks, it is challenging to automate the process. We propose a multi-dimensional mining framework that can be used to identify a concise set of anomalies from massive traffic monitoring data, and further overlay, contrast, and explore such anomalies in multi-dimensional space.","abstract_html":"Mining traffic anomalies. Identification and characterization of traffic anomalies on massive road networks is a vital component of traffic monitoring [44]. Anomaly identification can be used to reduce congestion, increase safety, and provide transportation engineers with better information for traffic forecasting and road network design. However, due to the size, complexity and dynamics of such transportation networks, it is challenging to automate the process. We propose a multi-dimensional mining framework that can be used to identify a concise set of anomalies from massive traffic monitoring data, and further overlay, contrast, and explore such anomalies in multi-dimensional space.","abstract_has_math":false,"creators":["Gonzalez, Hector"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:20:32Z","date_published":"2015-09-25T20:20:32Z","updated_at":"2026-07-22T22:26:17Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3314775"],"render_values":[{"text":"(MiAaPQ)AAI3314775","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81810","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Gonzalez, Hector"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:20:32Z","10000-01-01","2008"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81810","(MiAaPQ)AAI3314775"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Mining traffic anomalies. Identification and characterization of traffic anomalies on massive road networks is a vital component of traffic monitoring [44]. Anomaly identification can be used to reduce congestion, increase safety, and provide transportation engineers with better information for traffic forecasting and road network design. However, due to the size, complexity and dynamics of such transportation networks, it is challenging to automate the process. We propose a multi-dimensional mining framework that can be used to identify a concise set of anomalies from massive traffic monitoring data, and further overlay, contrast, and explore such anomalies in multi-dimensional space.","Made available in DSpace on 2015-09-25T20:20:32Z (GMT). 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Anomaly identification can be used to reduce congestion, increase safety, and provide transportation engineers with better information for traffic forecasting and road network design. However, due to the size, complexity and dynamics of such transportation networks, it is challenging to automate the process. We propose a multi-dimensional mining framework that can be used to identify a concise set of anomalies from massive traffic monitoring data, and further overlay, contrast, and explore such anomalies in multi-dimensional space.","Made available in DSpace on 2015-09-25T20:20:32Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3314775.pdf: 3249111 bytes, checksum: f32663e1d63b4263b5d8a9f4481ce17b (MD5) Previous issue date: 2008","Embargo set by: Seth Robbins for item 83091 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","152 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008."],"dc:identifier":["http://hdl.handle.net/2142/81810","(MiAaPQ)AAI3314775"],"dc:language":["eng"],"dc:subject":["Computer Science"],"dc:title":["Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:17Z"}