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High performance spatial visualization of traffic data

Author: Shashi ShekharChang Tien LuAlan LiuUniversity of Minnesota. Intelligent Transportation Systems Institute.University of Minnesota. Department of Computer Science and Engineering.All authors
Publisher: [Minneapolis, Minn.] : University of Minnesota, Center for Transportation Studies ; [Springfield, Va.] : [Available from the National Technical Information Service], [2004]
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Current visualizations techniques for identifying performance bottlenecks with loop-detector traffic data are not sufficient for large data sets to create interactive visualization and analysis of possible scenarios. This study seeks to develop a more effective means of processing data obtained at the Traffic Management Center (TMC) to identify recurring patterns in the traffic data that may be being lost in current  Read more...
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Genre/Form: Databases
Additional Physical Format: Online version:
Shekhar, Shashi.
High performance spatial visualization of traffic data.
[Minneapolis, Minn.] : University of Minnesota, Center for Transportation Studies ; [Springfield, Va. : Available from the National Technical Information Service, 2004]
(OCoLC)762189517
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Shashi Shekhar; Chang Tien Lu; Alan Liu; University of Minnesota. Intelligent Transportation Systems Institute.; University of Minnesota. Department of Computer Science and Engineering.; Minnesota. Department of Transportation. Research Service Section.
OCLC Number: 56336696
Notes: "July 2004."
Description: 70, 4 pages : illustrations, charts ; 28 cm
Responsibility: Shashi Shekhar, Chang-Tien Lu, Alan Liu.

Abstract:

Current visualizations techniques for identifying performance bottlenecks with loop-detector traffic data are not sufficient for large data sets to create interactive visualization and analysis of possible scenarios. This study seeks to develop a more effective means of processing data obtained at the Traffic Management Center (TMC) to identify recurring patterns in the traffic data that may be being lost in current data collection process. The final objective is to create a software prototype for analysis.

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