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Published Articles >> Table of Contents >> Abstract
21st International Conference on Data Engineering (ICDE'05)
pp. 1061-1072
Knowledge Discovery from Transportation Network Data
Wei Jiang, Purdue University
Jaideep Vaidya, Rutgers University and CIMIC
Zahir Balaporia, Schneider National, Inc.
Chris Clifton, Purdue University
Brett Banich
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.82
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| Abstract |
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Transportation and Logistics are a major sector of the
economy, however data analysis in this domain has remained
largely in the province of optimization. The potential
of data mining and knowledge discovery techniques
is largely untapped. Transportation networks are naturally
represented as graphs. This paper explores the problems
in mining of transportation network graphs: We hope to
find how current techniques both succeed and fail on this
problem, and from the failures, we hope to present new
challenges for data mining. Experimental results from applying
both existing graph mining and conventional data
mining techniques to real transportation network data are
provided, including new approaches to making these techniques
applicable to the problems. Reasons why these techniques
are not appropriate are discussed. We also suggest
several challenging problems to precipitate research and
galvanize future work in this area.
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 Additional Information
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Citation:
Wei Jiang, Jaideep Vaidya, Zahir Balaporia, Chris Clifton, Brett Banich,
"Knowledge Discovery from Transportation Network Data,"
icde,
pp. 1061-1072,
21st International Conference on Data Engineering (ICDE'05),
2005
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