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University of Illinois at Urbana-Champaign
Mining Massive Moving Object Datasets From RFID Flow Analysis to Traffic Mining
Abstract
dc:descriptionMining 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.
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Discipline thesis:degree_discipline
- Computer Science
- Grantor
- University of Illinois at Urbana-Champaign
- Year dc:date
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Gonzalez, Hector
- Contributors dc:contributor
-
- Han, Jiawei
Subjects
dc:subject × 1Rights
- Language dc:language
- eng
Identifiers
dc:identifier.*- Identifier
- (MiAaPQ)AAI3314775
- OAI identifier oai:identifier
- oai:www.ideals.illinois.edu:2142/81810