Keynote talk at 2nd International Conference on Automotive User Interfaces and Interactive
Vehicular Applications (AutomotiveUI 2010), 11-12 November, 2010, Pittsburgh, PA
What To Do With 100
Million GPS Points
John Krumm
Microsoft Research
Redmond, WA USA
Tire treads! 
Joke
The one about the guy who joins a monastery
History of My Cars
1974 VW Super Beetle
1992 Nissan 300ZX
1986 Honda CRX
2003 BMW M3
6 years
700 vehicles/people
155 million GPS points
GPS Data
Jon Froehlich – U. Washington
GPS Data Sources
Garmin Geko 201
~ $120 each
10,000 point memory
55 units
RoyalTek RBT-2300
~ $55 each
400,000 point memory
300 units
317 “Regular” Vehicles
252 Paratransit Vans
64“Regular” People
99 Microsoft Shuttles
New updates are ready to install
Updates for your computer have been downloaded
from Windows Update. Click here to review these
updates and install them.
With Eric Horvitz, Microsoft Research
Example Analysis: Driver Destinations
250 volunteer drivers
2-4 weeks each
13,000 trips
Private vehicle data around
Seattle, WA USA
Destinations vs. Time of Day
New Destinations vs. Time of Day
Driver Destinations
Destinations vs. Day of Week
New Destinations vs. Day of Week
Applications
• When to suggest a new destination
• When to offer routing help
• When to be quiet
New Destinations
4
Rate of decline vs. demographics
• Single vs. partner – no difference
• Children vs. no children – no difference
• Extended family nearby or not – no difference
• Gender – women decline faster than men
New Destinations Visited
3.5
3
2.5
2
1.5
1
0.5
0
0
1
2
3
4
5
6
Days Into Survey
7
8
9
10
11
12
Data Collection
Incentives
• 1 in 100 chance of winning $200 MP3 player
• Your choice of any Microsoft product
• $ 0.50/day
• Map with your data
Privacy
• Easy to find volunteers, some unsolicited
• 21 of 32 agreed to anonymous sharing on Web
(http://research.microsoft.com/~jckrumm/GPSData2009/)
Other Sources?
• Taxis
• Delivery (packages, pizza)
• Garbage trucks
Who Is Using Data Like This?
Dash Express
(discontinued)
Inrix
(going strong)
Use GPS data to assess traffic
WAZE
GPS to assess traffic
and make maps
OpenStreetMap
GPS + aerial images +
out-of-copyright maps
+ people
What Are We Doing?
Prediction – Where are you
going? When will you be home?
Privacy – What’s the risk of
GPS data? How do people
feel? How to solve?
Roads – How can we infer a
road map from GPS data?
New updates are ready to install
Updates for your computer have been downloaded
from Windows Update. Click here to review these
updates and install them.
Location Prediction
Navigation Device as Constant Companion
• Gas prices
• Traffic
• Flow
• Accidents
Better with
• Road construction
location prediction
• Points of interest
• Available parking
• Advertising
Use Route Planning?
GM’s OnStar subscribers ask for
directions for about 1% of their
trips1,2
Video: Why Men Don’t Ask for Directions
1
2
Automotive Engineering International, July 2008, pp. 34-36
US DOT (http://www.bts.gov/programs/national_household_travel_survey/daily_travel.html)
Efficient Driving
(1)
(2)
p=0.63 that a cellto-cell transition will
decrease time to
destination (from
observing GPS trips)
(3)
Median Error of Destination
Prediction
30
Error (km)
20
10
0
0
0.1
0.2
0.3 0.4 0.5 0.6 0.7
Fraction of Trip Completed
0.8
0.9
1
?
p = (.63)(.63)(.63)(1-.63)(1-.63)
NEW UPDATES AVAILABLE!!
A Relevant Cartoon
Other Destination Clues
Destination Frequency vs. Ground Cover
emergent herbacous wetlands
woody wetlands
orchard
perennial ice
small grains
row crops
bare rock
fallow
urban
high intensity residential
transitional
quarry
pasture
water
grasslands
mixed forest
shrubland
deciduous forest
evergreen forest
low intensity residential
commercial
US Geological Survey
0
0.1
0.2
0.3
0.4
Normalized Frequency
Ground Cover
Personal Destinations
Prediction Error vs. Trip Fraction
6000
0.25
0.2
0.15
0.1
0.05
0
0-4
5-9
10-14 15-19 20-24 25-29 30-34 35-39
Trip Tim e (m inutes)
> 39
Median Prediction Error (meters)
Normalized Frequency
Trip Tim e Distribution
5000
Complete data model
Open-world model
Simple closed-world model
4000
3000
2000
1000
0
Trip Time Distribution
With Eric Horvitz, Microsoft Research
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Trip Fraction
Result: 2 km median error after ½ of trip
1
Better Prediction?
Time of day and day of week
Influence of destination types
p(restaurant) > p(dentist)
Route prediction
Time and Location Priors
From “American Time Use Survey” (ATUS) 2003-2008
1
Bus
0.9
Subway/train
Walking
0.8
Car, truck, or motorcycle (driver)
Someone else's home
Unspecified mode of transportation
0.7
Airplane
Other mode of transportation
0.6
Taxi/limousine service
0.5
Bicycle
Respondent 's workplace
Boat/ferry
0.4
Car, truck, or motorcycle (passenger)
Post Office
0.3
Bank
Library
0.2
Unspecified place
Respondent 's home or yard
Gym/health club
0.1
Place of worship
Grocery store
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Hour of Day
14
15
16
17
18
19
20
21
22
23
Outdoors away from home
Joke
The one about the prisoners telling jokes
Route Prediction
Predict turns with
Markov model
Basic Approach
Markov Model
tycdbgwdfcdbeaxcdbwadcdbgwaxoxcdbgpmccdbwv
tycdbgwdfcdbeaxcdbwadcdbgwaxoxcdbgpmccdbwv
What Comes After "cdb"?
0.6
0.5
3rd
0.4
probability
order Markov model
trained from data
0.3
0.2
0.1
New updates are ready to install
0
e
g
These are some of our best updates
ever. We are sure
w you will enjoy them.
Basic Approach
Markov Model
tycdbgwdfcdbeaxcdbwadcdbgwaxoxcdbgpmccdbwv
tycdbgwdfcdbeaxcdbwadcdbgwaxoxcdbgpmccdbwv
What Comes After "cdb"?
0.6
0.5
3rd
0.4
probability
order Markov model
trained from data
0.3
0.2
0.1
0
e
g
w
M-Ahead Prediction Accuracy
Prediction Accuracy vs. Road Segments Predicted
1
0.9
0.8
Prediction Accuracy
0.7
0.6
Each road segment is 237.5
meters (0.15 miles)
0.5
Experimental Result
0.4
Random Guess (direction known)
0.3
Random Guess (direction unknown)
0.2
0.1
0
1
2
3
4
5
6
7
Road Segments Predicted into Future
8
9
10
Prediction Sequence for Route
String together sequence of turn predictions to predict whole route
Generic Turn Prediction
Predict which way someone will turn
• No record of their behavior
• No record of anyone’s behavior at this turn
Turn Proportions
GREAT UPDATES, REALLY!
“Tube” sensor
for traffic counts
Basic Idea
Assume more popular
Assume less popular
Candidate destinations (83,353 road segments)
Candidate destinations (close up)
Basic Algorithm
Find which destinations are best reached by which turn direction
Works OK
0.250
Median Proportion Error
All Turn Counts
Most Recent Turn Counts
0.200
• “Trip Time Weights” performs best
• Better on most recent turn counts
0.150
0.100
0.050
0.000
Basic
Triangles
Trip Time
Probabilities
Trip Time Weights
Survey: Estimate when you will be
• Home sleeping
• Home awake
• Away from home
Time of Day (hour)
How Good are People
at Predicting Home/Away?
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Sunday
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
awake home
away
away
away
away
away
away
away
away
away
awake home
awake home
awake home
awake home
awake home
awake home
awake home
sleeping
Monday
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
awake home
awake home
awake home
away
away
away
away
away
away
away
awake home
awake home
awake home
awake home
awake home
awake home
sleeping
sleeping
Tuesday
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
awake home
awake home
away
away
away
away
away
away
away
away
awake home
awake home
awake home
awake home
awake home
sleeping
sleeping
Day of Week
Wedneday
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
awake home
awake home
awake home
away
away
away
away
away
away
awake home
awake home
awake home
awake home
awake home
awake home
sleeping
sleeping
Thursday
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
awake home
awake home
awake home
away
away
away
away
away
away
away
away
away
awake home
awake home
awake home
awake home
sleeping
• How good are people at anticipating their home/away state?
• 34-person user survey
• Goal: use GPS to control home heating for better efficiency
Friday
sleeping
sleeping
sleeping
sleeping
sleeping
awake home
awake home
away
away
away
away
away
away
away
away
away
away
awake home
awake home
awake home
awake home
awake home
awake home
awake home
Saturday
awake home
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
sleeping
awake home
awake home
awake home
awake home
awake home
away
away
away
away
away
awake home
awake home
awake home
awake home
awake home
We Can Do Better
Time of Day
Sunday
GPS Study
• 12 households
• 34 people
• GPS logger for 8 weeks
12:00 AM
12:30 AM
1:00 AM
1:30 AM
2:00 AM
2:30 AM
3:00 AM
3:30 AM
4:00 AM
4:30 AM
5:00 AM
5:30 AM
6:00 AM
6:30 AM
7:00 AM
7:30 AM
8:00 AM
8:30 AM
9:00 AM
9:30 AM
10:00 AM
10:30 AM
11:00 AM
11:30 AM
12:00 PM
12:30 PM
1:00 PM
1:30 PM
2:00 PM
2:30 PM
3:00 PM
3:30 PM
4:00 PM
4:30 PM
5:00 PM
5:30 PM
6:00 PM
6:30 PM
7:00 PM
7:30 PM
8:00 PM
8:30 PM
9:00 PM
9:30 PM
10:00 PM
10:30 PM
11:00 PM
11:30 PM
Monday
0.050
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.149
0.376
0.600
0.567
0.383
0.400
0.388
0.376
0.400
0.721
0.750
0.600
0.600
0.600
0.600
0.595
0.333
0.333
0.305
0.167
0.167
0.167
0.167
0.108
0.000
0.000
0.000
0.000
Tuesday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.807
1.000
1.000
1.000
0.833
0.857
0.857
0.993
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.985
1.000
0.897
0.500
0.600
0.368
0.200
0.314
0.500
0.429
0.429
0.302
0.286
0.172
0.143
0.143
0.000
0.000
0.000
0.000
Day of Week
Wednesday
Thursday
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.000
0.012
0.000
0.035
0.000
0.075
0.000
0.133
0.000
0.209
0.000
0.300
0.000
0.404
0.000
0.515
0.000
0.625
0.000
0.728
0.371
0.812
0.427
0.934
0.583
0.999
0.649
0.294
0.797
0.091
0.560
0.200
0.546
0.443
0.429
0.833
0.637
0.833
0.804
0.714
0.625
0.714
0.581
0.714
0.714
0.714
0.714
0.714
0.714
0.714
0.667
0.714
0.667
0.667
0.729
0.650
0.712
0.571
0.440
0.709
0.336
0.612
0.251
0.429
0.375
0.510
0.599
0.532
0.429
0.418
0.371
0.250
0.384
0.220
0.286
0.125
0.286
0.125
0.206
0.053
0.143
0.000
0.143
0.000
0.200
0.000
0.250
0.000
0.000
Friday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.170
0.461
0.565
0.587
0.379
0.090
0.000
0.341
0.571
0.400
0.400
0.703
0.750
0.750
0.750
0.714
0.714
0.559
0.498
0.429
0.519
0.506
0.571
0.460
0.429
0.313
0.250
0.290
0.343
0.202
0.143
0.143
0.000
0.000
Saturday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.007
0.032
0.084
0.171
0.298
0.471
0.692
0.962
0.964
0.875
0.875
0.810
0.714
0.514
0.571
0.571
0.574
0.541
0.400
0.500
0.352
0.310
0.315
0.250
0.328
0.250
0.151
0.142
0.125
0.125
0.125
0.125
0.290
0.351
0.375
0.375
0.375
0.333
0.400
0.667
0.667
0.453
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.182
0.200
0.200
0.011
0.101
0.189
0.368
0.375
0.348
0.287
0.345
0.143
0.208
0.427
0.375
0.148
0.125
0.143
0.000
0.143
0.053
0.000
0.094
0.143
0.143
0.143
0.143
0.143
0.143
0.143
Generic Weekday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.002
0.073
0.132
0.061
0.002
0.000
0.000
0.219
0.322
0.252
0.255
0.325
0.357
0.324
0.294
0.283
0.250
0.160
0.099
0.043
0.000
0.007
0.125
0.085
0.080
0.073
0.081
0.083
0.095
0.053
0.000
0.000
0.000
0.000
Learned probability of being away from home
• Function of time of day and day of week
• Much better at predicting home/away than persons themselves
Prediction Summary
Predict Destination
• Efficient driving
• Other cues
Efficient Driving
US Geological Survey
Predict Route
• Markov model
• Generic
Time of Day
Sunday
12:00 AM
12:30 AM
1:00 AM
1:30 AM
2:00 AM
2:30 AM
3:00 AM
3:30 AM
4:00 AM
4:30 AM
5:00 AM
5:30 AM
6:00 AM
6:30 AM
7:00 AM
7:30 AM
8:00 AM
8:30 AM
9:00 AM
9:30 AM
10:00 AM
10:30 AM
11:00 AM
11:30 AM
12:00 PM
12:30 PM
1:00 PM
1:30 PM
2:00 PM
2:30 PM
3:00 PM
3:30 PM
4:00 PM
4:30 PM
5:00 PM
5:30 PM
6:00 PM
6:30 PM
7:00 PM
7:30 PM
8:00 PM
8:30 PM
9:00 PM
9:30 PM
10:00 PM
10:30 PM
11:00 PM
11:30 PM
Monday
0.050
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.149
0.376
0.600
0.567
0.383
0.400
0.388
0.376
0.400
0.721
0.750
0.600
0.600
0.600
0.600
0.595
0.333
0.333
0.305
0.167
0.167
0.167
0.167
0.108
0.000
0.000
0.000
0.000
Tuesday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.807
1.000
1.000
1.000
0.833
0.857
0.857
0.993
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.985
1.000
0.897
0.500
0.600
0.368
0.200
0.314
0.500
0.429
0.429
0.302
0.286
0.172
0.143
0.143
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.002
0.012
0.035
0.075
0.133
0.209
0.300
0.404
0.515
0.625
0.728
0.812
0.934
0.999
0.294
0.091
0.200
0.443
0.833
0.833
0.714
0.714
0.714
0.714
0.714
0.714
0.714
0.667
0.650
0.571
0.709
0.612
0.429
0.510
0.532
0.418
0.250
0.220
0.125
0.125
0.053
0.000
0.000
0.000
0.000
Day of Week
Wednesday
Thursday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.371
0.427
0.583
0.649
0.797
0.560
0.546
0.429
0.637
0.804
0.625
0.581
0.714
0.714
0.714
0.667
0.667
0.729
0.712
0.440
0.336
0.251
0.375
0.599
0.429
0.371
0.384
0.286
0.286
0.206
0.143
0.143
0.200
0.250
0.000
Friday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.170
0.461
0.565
0.587
0.379
0.090
0.000
0.341
0.571
0.400
0.400
0.703
0.750
0.750
0.750
0.714
0.714
0.559
0.498
0.429
0.519
0.506
0.571
0.460
0.429
0.313
0.250
0.290
0.343
0.202
0.143
0.143
0.000
0.000
Saturday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.007
0.032
0.084
0.171
0.298
0.471
0.692
0.962
0.964
0.875
0.875
0.810
0.714
0.514
0.571
0.571
0.574
0.541
0.400
0.500
0.352
0.310
0.315
0.250
0.328
0.250
0.151
0.142
0.125
0.125
0.125
0.125
0.290
0.351
0.375
0.375
0.375
0.333
0.400
0.667
0.667
0.453
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.182
0.200
0.200
0.011
0.101
0.189
0.368
0.375
0.348
0.287
0.345
0.143
0.208
0.427
0.375
0.148
0.125
0.143
0.000
0.143
0.053
0.000
0.094
0.143
0.143
0.143
0.143
0.143
0.143
0.143
Generic Weekday
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.002
0.073
0.132
0.061
0.002
0.000
0.000
0.219
0.322
0.252
0.255
0.325
0.357
0.324
0.294
0.283
0.250
0.160
0.099
0.043
0.000
0.007
0.125
0.085
0.080
0.073
0.081
0.083
0.095
0.053
0.000
0.000
0.000
0.000
Predict Occupancy
Markov Route
Generic Turn Proportions
1
0.8
Future
• Behavioral priors
• Prediction service
0.6
0.4
0.2
0
0 2 4 6 8 10 12 14 16 18 20 22
Hour of Day
Prediction Service
Joke
The one about the ugly baby
Location Privacy
How do people feel about
location privacy?
What are the risks?
Solve location privacy with
fake trips?
People Don’t Care About Location Privacy
• 74 U. Cambridge CS students
• Would accept £10 to reveal 28 days of measured locations (£20 for commercial use) (1)
• 226 Microsoft employees
• 14 days of GPS tracks in return for 1 in 100 chance for $200 MP3 player
• 62 Microsoft employees
• Only 21% insisted on not sharing GPS data outside
• 11 with location-sensitive message service in Seattle
• Privacy concerns fairly light (2)
• 55 Finland interviews on location-aware services
• “It did not occur to most of the interviewees that they could be located while
using the service.” (3)
(1)
Danezis, G., S. Lewis, and R. Anderson.
How Much is Location Privacy
Worth? in Fourth Workshop on the
Economics of Information Security.
2005. Harvard University.
(2) Iachello,
G., et al. Control, Deception, and
Communication: Evaluating the Deployment
of a Location-Enhanced Messaging Service.
in UbiComp 2005: Ubiquitous Computing.
2005. Tokyo, Japan.
(3) Kaasinen,
E., User Needs for LocationAware Mobile Services. Personal and
Ubiquitous Computing, 2003. 7(1): p. 70-79.
Real Data at Stake
Collected GPS data from 32 people in
12 households over 2 months
Visit 1
Visit 2
Two months
With A.J. Brush and James Scott, Microsoft Research
Willingness to Share
21/32 participants signed consent forms allowing us
to publicly share an anonymized version of the data
they collected during the study with data removed
around their home.
This data is now available online
http://research.microsoft.com/~jckrumm/GPSData2009/
New updates are ready to install
We know where you live
JOHN KRUMM of REDMOND, WASHINGTON
Favorite Location Services
Would you trade your GPS data to Microsoft for one of these services?
All participants (32) said they would trade for at least one service.
Help determine where bus routes should be
Tell you about traffic jams before you get there
Tell drivers where traffic is slow
Control your home thermostat to save energy
Help businesses locate to high traffic areas
Estimates of your impact on the environment
Recommend local places you might like
…
94%
91%
88%
72%
72%
65%
62%
…
Sell Us Your GPS Data
How much would we (Microsoft) have to pay you for 1 month of your GPS data?
$10
roughly same result (2006)
$50
$100
$250
Favorite Obfuscation Methods
Mix with nearby
others’ data
16
Delete around
home
Add random noise
Discretize
Subsample in
time
15
14
12
10
8
8
7
6
4
2
2
0
0
Mixing
Delete home
Random noise
Discretize
Subsample
What is the Risk?
1. Can anonymous GPS tracks be
used to infer someone’s
identity?
2. If so, how much do we have to
corrupt the data to stop the
attack?
Anonymized GPS tracks
Privacy Attack
Anonymized GPS
tracks
Infer home location
Reverse white pages
for identity
Find Home Location
Last Destination – median of last destination before 3 a.m.
Median error = 60.7 meters
(Algorithm 1 of 4 tried)
Find Who Lives There
New updates are ready to install
know you’re at CMU in GATES-HILLMAN
Reverse White Pages lookup (free public API fromWeMicrosoft)
6115 in PITTSBURGH, PENNSYLVANIA
GPS Tracks to Home Address/Identity
GPS Tracks
MapPoint Web
Service reverse
geocoding
Home
Location (60
meters)
Home
Address
(12%)
Identity (5%)
Windows Live
Search reverse
white pages
Why Not Better?
• Measurements
• Inaccurate GPS
• Missing GPS (parking structure)
• Error in home-finding algorithms
• Database
• Inaccurate reverse geocoding
• Outdated/inaccurate white pages
• Subject Behavior
• Parking away from home
• Multiunit buildings
White Pages Search for Nam e at Address
Different Name at
Address
11%
M ultiunit Building
13%
Successfully Found
33%
Address Not Found
43%
Look up name associated with
subjects’ self reported address
Joke
The one about the jewelry
Countermeasure: Add Noise
original
Effect of added noise
on address-finding rate
σ= 50 meters noise added
Countermeasure: Discretize
original
Effect of discretization
on address-finding rate
snap to 50 meter grid
Countermeasure: Cloak Home
1. Pick a random circle center within “r” meters of home
2. Delete all points in circle with radius “R”
Anonymized GPS Data
1. Simple algorithms can extract identity from anonymized GPS tracks
2. Corruption-based countermeasures need lots of corruption
False Trips
Make N false location reports
to confuse attacker
GPS mobile device
true report
false reports
1000 False Trips
Learn simulation
parameters from
GPS data
Realistic
• Endpoints
• Speeds
• Routes
• GPS noise
Privacy Summary
People don’t care about
location privacy
People are willing to
trade their location data
for money or services
It takes a lot of
obfuscation to protect
anonymized GPS data
We can potentially
confuse an attacker
with realistic false trips
GPS to Road Map
Raw GPS traces
Road map
Crowdsource GPS traces
from everyday vehicles
Why Do This?
Data Expensive
Roads Change
Navteq
New updates are ready to install
29 October 2009
Tele Atlas
I highly recommend
these updates. – Bill
Gates
Find Lane Structure
colored by separate trips
With Yihua Chen, University of Washington (now at Google)
• 4 lanes on the left and 2
lanes on the right
• No clear cluster can be
observed due to GPS
noise
Gaussian Mixture Model
Count and locate lanes
noisy GPS traces
Gaussian mixture
sampling cross sections
For a 2-lane road
Gaussian Mixture Model
Specialized Gaussian Mixture Model
• Equal lane widths
• Equal GPS noise in each lane
• Complexity penalty sensitive to expected lane width
• New EM algorithm for fitting
Advantages
• Faster to fit model
• Better at counting lanes
• More consistent results from lane to lane
(1)
(2)
(3)
Find Intersections
Shape descriptor
With Alireza Fathi, Georgia Tech
Increment bins where
GPS trace intersects
Detected Intersections
Intersection detector
Detected intersections and roads
GPS data
ROC curve
From GPS to Routable Road Map
Refine noisy GPS data into a routable road network
(1) raw gps
With Lili Cao, UC Santa Barbara
(2) clarified traces
(3) merged into roads
Routable Road Map
Demo (Lili Cao)
Map Summary
Lane structure
Intersection detector
Routable road map
Future
• Road names
• 1-way vs. 2-way
• Turn restrictions
• Traffic controls
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%
Four routes from A to B, all different:
Empirically
fastest
MapPoint
plan
Shortest
distance
Driver’s
route
With Julie Letchner, U. Washington (now Microsoft) and Eric Horvitz, Microsoft Research
Personalized Routes
New routes:
• Sensitive to traffic speeds
• Prefer previously driven roads
• More often the route actually driven
Personalized Routes Next
Drivers have many criteria
for choosing a route
Minimize: cost = α1(driving time) + α2(# of left turns) + α3(complexity) +
α4(scenery) + α5(# of traffic lights) + …
Infer αi from multiple trip observations
Why Risk Death With
a Competing Product?
2001-2006 highway traffic fatalities
Trip time
Probability of Death
0.001%
Probability of Death
100%
Joke
The one about the doctor who phones his patient
End
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