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What to Do With Thousands of GPS Tracks

John Krumm, PhDMicrosoft Research

Redmond, WA

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GPS DataMicrosoft Multiperson Location Survey (MSMLS)

55 GPS receivers227 subjects1.77 million points95,000 miles153,000 kilometers12,507 tripsHome addresses & demographic data

Greater Seattle Seattle Downtown Close-up

Garmin Geko 201$11510,000 point memorymedian recording interval

6 seconds63 meters

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GPS Projects

• Personalized Routes• Predestination• Location Privacy

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Personalized Routes

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%

Empirically fastestShortest distance

MapPoint plan

Driver’s route

One Driver A to B:

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.

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Preferable Routes

One trip from GPS data

• Deflate cost of previously driven roads• Tested on ~2500 trips

• 47% of computed routes matched actual• Only 11% of trips duplicated in data

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Dynamic Map Matching

measured GPS points

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Goal: Infer actual route from noisy location data

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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.

Results on traditional problem cases

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PredestinationWhere do you want to go today? We already know.

Traffic WarningDestination 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.

Going to the airport? Park with us for $8/day!

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.

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Predestination• Previous destinations• Preferred ground cover• Efficient driving• Anticipated trip times

USGS Ground Cover: swamps unpopular as destination

(1) (2) (3)

Median error = 2 kilometers at halfway point of trip

John Krumm and Eric Horvitz, "Driver Destination Models", Eleventh International Conference on User Modeling (UM 2007), June 25-27, 2007, Corfu, Greece.

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Location Privacy

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.

Why reveal your location to a 3rd party?

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Attack Outline

Relative Probability of Home vs. Time of Day

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8 a.m. 6 p.m. Median error = 61 meters

Correct name on 5%

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Computational Countermeasures

Uncorrupted Data Spatial Cloaking

Gaussian Noise, σ = 50 m Discretize, Δ = 50 mMention this talk at any participating* Pizza Hut and receive free breadsticks!* There are actually no participating Pizza Huts.

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How Much Corruption?

Accuracy Effects

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Imprecision Effects

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Gaussian Noise, σ = 50 m Discretize, Δ = 50 m

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Donemeasured

GPS points

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GPS Data Personalized Routes Map Matching

Driver Models Predestination Location Privacy

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