Mining GPS Traces to Improve Digital Maps
Digital maps are an essential component of any automobile navigation
system, but existing maps are both inaccurate and incomplete. However,
the availability of accurate global positioning systems suggests an
approach to improving them. This project focuses on collecting
time-tagged position traces from driven vehicles and using
these traces to discover useful knowledge about the road network and
Applications include revising estimates for the centerline of a road
segment, inferring the number and locations of lanes on that segment,
finding lane merges and splits, predicting a
driver's travel time for the segment, calculating stopping distance
estimates, and determining the presence and
type of traffic controls at an intersection. Initial
results suggest that data mining of vehicle traces can substantially
improve the accuracy and completeness of digital maps, and that it can
provide personalized models of driver behavior. Further work with real-time
data for dynamic map updates is planned.
This research was funded by DaimlerChrysler Research and Technology.
Contributors to the Project
Professor Pat Langley
Dr. Seth Rogers
Dr. Stefan Schroedl
Dr. Kiri Wagstaff
Schroedl, S., Wagstaff, K., Rogers, S., Langley, P., & Wilson, C.
(2004). Mining GPS traces for map refinement. Knowledge Discovery
and Data Mining, 9, 59-87.
Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001).
Constrained k-means clustering with background knowledge.
Proceedings of the Eighteenth International Conference on Machine
Learning (pp. 577-584). Williams College, MA: Morgan Kaufmann.
Rogers, S. (2000).
Creating and evaluating highly accurate maps with probe vehicles.
Proceedings of the IEEE
Conference on Intelligent Transportation Systems.
Pribe, C., & Rogers, S. (1999).
Learning to associate driver behavior with traffic controls.
Transportation Research Record, No. 1679, 95-100.
Rogers, S., Langley, P., & Wilson, C. (1999).
Mining GPS data to augment road models.
Proceedings of the Fifth International Conference on Knowledge
Discovery and Data Mining (pp. 104-113). San Diego, CA: ACM Press.
Handley, S., Langley, P., & Rauscher, F. A. (1998).
Learning to predict the duration of an automobile trip.
Proceedings of the Fourth International Conference on Knowledge
Discovery and Data Mining (pp. 219-223). New York: AAAI Press.
Wilson, C., Rogers, S., & Weisenburger, S. (1998).
The potential of precision maps in intelligent vehicles.
Proceedings of the 1998 IEEE International Conference on Intelligent
Vehicles (pp. 419-422). Stuttgart, Germany
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