Visual exploration of large traffic database using traffic cubes

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Publication Type thesis
School or College College of Social & Behavioral Science
Department Geography
Author Song, Ying
Title Visual exploration of large traffic database using traffic cubes
Date 2010-04-26
Description Advances in data collection tools and computation abilities result in a significant increase of capabilities for both collecting and generating data. The consequent growth of available data generates an urgent need for techniques to transform the massive amount of collected data into useful information. This thesis develops traffic-cube based visualization techniques, and demonstrates the efficiency of those techniques as an approach of mining traffic flow data. The basis of these techniques is a data mining technique known as data cube, which is an array of values defined across multiple measurement dimensions that allows rapid, user-interactive aggregation and cross-tabulations of data. The traffic cube is an extension of the data cube that facilitates these operations and is usually used to organize and manage traffic flow data. In this thesis, the traffic cube is defined by three dimensions: the clock time (T), the calendar date (D), and the spatial location (S) of the recorded traffic count. For each dimension, a hierarchy is designed for data aggregation; and we visualize the aggregated two-dimensional slices of the original traffic data cube to extract useful information about traffic situation.
Type Text
Publisher University of Utah
Subject Traffic flow; Computer simulation
Dissertation Institution University of Utah
Dissertation Name MS
Language eng
Relation is Version of Digital reproduction of "Visual exploration of large traffic database using traffic cubes" J. Willard Marriott Library Special Collections HE136.5 2010 .S66
Rights Management © Ying Song, To comply with copyright, the file for this work may be restricted to The University of Utah campus libraries pending author permission.
Format Medium application/pdf
Format Extent 94,112 bytes
Identifier us-etd2,158930
Source Original: University of Utah J. Willard Marriott Library Special Collections
Conversion Specifications Original scanned on Epson GT-30000 as 400 dpi to pdf using ABBYY FineReader 9.0 Professional Edition.
ARK ark:/87278/s6t15j35
Setname ir_etd
Date Created 2012-04-23
Date Modified 2018-03-23
ID 192334
Reference URL
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