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oai:www.ideals.illinois.edu:2142/81810 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Han, Jiawei Gonzalez, Hector 2015-09-25T20:20:32Z 2015-09-25T20:20:32Z 10000-01-01 2008 2008 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). 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. http://hdl.handle.net/2142/81810 (MiAaPQ)AAI3314775 eng Computer Science Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.