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What to Do With Thousands of GPS Tracks. John Krumm, PhD Microsoft Research Redmond, WA. GPS Data. Microsoft Multiperson Location Survey (MSMLS). Garmin Geko 201 $115 10,000 point memory median recording interval 6 seconds 63 meters. 55 GPS receivers 227 subjects
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What to Do With Thousands of GPS Tracks John Krumm, PhD Microsoft Research Redmond, WA
GPS Data Microsoft Multiperson Location Survey (MSMLS) • Garmin Geko 201 • $115 • 10,000 point memory • median recording interval • 6 seconds • 63 meters 55 GPS receivers 227 subjects 1.77 million points 95,000 miles 153,000 kilometers 12,507 trips Home addresses & demographic data Seattle Downtown Close-up Greater Seattle
GPS Projects • Personalized Routes • Predestination • Location Privacy
Personalized Routes One Driver A to B: MapPoint plan Empirically fastest Shortest distance Driver’s route Percentage of trips in our data for which the driver’s actual route matched the… Shortest route: 27% Fastest route: 31% MapPoint route: 39% Neither shortest nor fastest: 60% Julia Letchner, John Krumm, and Eric Horvitz, "Trip Router with Individualized Preferences (TRIP): Incorporating Personalization into Route Planning", Eighteenth Conference on Innovative Applications of Artificial Intelligence (IAAI-06), July 2006.
Preferable Routes • Deflate cost of previously driven roads • Tested on ~2500 trips • 47% of computed routes matched actual • Only 11% of trips duplicated in data One trip from GPS data (Note: people who liked this slide also liked the next slide)
Dynamic Map Matching Goal: Infer actual route from noisy location data Results on traditional problem cases John Krumm, Julie Letchner, and Eric Horvitz, "Map Matching with Travel Time Constraints", Society of Automotive Engineers (SAE) 2007 World Congress, April 2007, Paper 2007-01-1102.
Going to the airport? Park with us for $8/day! Traffic Warning Destination Safeco Field(54% chance): 15-minute delay at I-405 & I-90. Suggest I-5 instead. Destination Seattle Center (31% chance): Broad St. closed. Suggest Denny Way instead. Predestination Where do you want to go today? We already know. Regular nav system Upcoming traffic Relevant ads Optimize hybrid charge/discharge John Krumm and Eric Horvitz, "Predestination: Inferring Destinations from Partial Trajectories", Eighth International Conference on Ubiquitous Computing (UbiComp 2006), September 2006.
Predestination • Previous destinations • Preferred ground cover • Efficient driving • Anticipated trip times USGS Ground Cover: swamps unpopular as destination Median error = 2 kilometers at halfway point of trip (1) (2) (3) John Krumm and Eric Horvitz, "Driver Destination Models", Eleventh International Conference on User Modeling (UM 2007), June 25-27, 2007, Corfu, Greece.
Location Privacy Why reveal your location to a 3rd party? Congestion Pricing Location Based Services Pay As You Drive (PAYD) Insurance Collaborative Traffic Probes (DASH) Research (London OpenStreetMap) John Krumm, "Inference Attacks on Location Tracks", Fifth International Conference on Pervasive Computing (Pervasive 2007), May 13-16, 2007, Toronto, Ontario, Canada.
Attack Outline Median error = 61 meters Correct name on 5%
Computational Countermeasures Uncorrupted Data Spatial Cloaking Gaussian Noise, σ = 50 m Discretize, Δ = 50 m Mention this talk at any participating* Pizza Hut and receive free breadsticks! * There are actually no participating Pizza Huts.
How Much Corruption? Discretize, Δ = 50 m Gaussian Noise, σ = 50 m
Done GPS Data Personalized Routes Map Matching Driver Models Predestination Location Privacy How’s my talk? 1-800-555-TALK HowsMyTalk.com