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Published Articles >> Table of Contents >> Abstract
7th International Conference on Mobile Data Management (MDM'06)
p. 50
ANNATTO: Adaptive Nearest Neighbor Queries in Travel Time Networks
Wei-Shinn Ku, University of Southern California, USA
Roger Zimmermann, University of Southern California, USA
Haojun Wang, University of Southern California, USA
Trung Nguyen, University of Southern California, USA
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MDM.2006.37
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| Abstract |
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Nearest neighbor (NN) searches represent an important
class of queries in geographic information systems (GIS).
Most nearest neighbor algorithms rely on static distance information
to compute NN queries (e.g., Euclidean distance
or spatial network distance). However, the final goal of a
user when performing an NN search is often to travel to
one of the search results. Based on this observation, finding
the nearest neighbors in terms of travel time is more
realistic than the actual distance. In the existing NN algorithms
dynamic real-time events (e.g., traffic congestions,
detours, etc.) are usually not considered and hence the
pre-computed nearest neighbor objects may not accurately
reflect the shortest travel time. In this demonstration we
present ANNATTO, a novel adaptive nearest neighbor query
model for travel time networks which integrates both spatial
networks and real-time traffic event information. The
ANNATTO system includes the implementation of a globalbased
adaptive nearest neighbor algorithm and a localbased
greedy nearest neighbor algorithm that both utilize
real-time traffic information to provide adaptive nearest
neighbor search results.
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 Additional Information
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Citation:
Wei-Shinn Ku, Roger Zimmermann, Haojun Wang, Trung Nguyen,
"ANNATTO: Adaptive Nearest Neighbor Queries in Travel Time Networks,"
mdm,
p. 50,
7th International Conference on Mobile Data Management (MDM'06),
2006
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