Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining
Gonzalez, Hector
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https://hdl.handle.net/2142/81810
Description
Title
Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining
Author(s)
Gonzalez, Hector
Issue Date
2008
Doctoral Committee Chair(s)
Han, Jiawei
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
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.
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