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Title:Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining
Author(s):Gonzalez, Hector
Doctoral Committee Chair(s):Han, Jiawei
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Computer Science
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.
Issue Date:2008
Type:Text
Language:English
Description:152 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.
URI:http://hdl.handle.net/2142/81810
Other Identifier(s):(MiAaPQ)AAI3314775
Date Available in IDEALS:2015-09-25
Date Deposited:2008


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