Learning Significant Locations and Predicting User ...Learning Significant Locations and Predicting...

GeorgiaTech

Learning Significant Locations and Predicting

User Movement with GPS

Daniel Ashbrook and Thad Starner

Contextual Computing Grouphttp://www.cc.gatech.edu/ccg

College of Computing, GVU CenterGeorgia Institute of Technology

Atlanta, GA USA

Daniel Ashbrook and Thad StarnerGeorgia

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Motivation

• Location is a very common form of context– easy to collect

– infer other pieces of context

• Most applications rely only on user’s current location


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Motivation

• How can we improve location context?• Look for patterns of movement and learn

user’s daily schedule– predict where user is going based on where

user has been

• Goal: computer can act as agent– offer suggestions at appropriate times– enable collaboration between colleagues


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Applications

• Potential applications for location prediction

• Single–user applications– system only knows about one user’s

movements

• Multi–user applications– system combines predictions for several

people


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Applications

• Single user: Pre–emptive Reminders– remind user at an appropriate time

– example: library book•try to determine if user will pass library today

•only then remind user to take book before leaving home


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Applications

• Single user: Wireless caching– wireless networks often unavailable

•lack of infrastructure

•radio shadows (buildings, subway)

– hide lack of connectivity by caching

– predict when caching will be insufficient•warn user

•suggest alternative routes


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Applications

• Single user: Wireless caching– cache even when network is available

•transmission power can increase with 4th power of distance in complex environments (i.e., city)

•cost can vary with network used, time of day

– prediction can allow savings•of battery power

•of money


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Applications

• Multi–user: Enabling collaboration– “Will I see Bob today?”

•compare the user’s and Bob’s schedules

•give yes or no answer

– Scheduling many–person meetings•find when most people are free and suggest a time

•also discover most convenient place to meet


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Applications

• Multi–user: Favor exchange– remotely coordinate favor trading

– example: FedEx/UPS package trading


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Related Work

• Bhattacharya — cell phone prediction

• Davis — prediction with ad–hoc networks

• Kortuem — Walid

• Marmasse — comMotion

• Liu — predictively caching network architecture

• Orwant — Doppelgänger

• Sparacino — Museum Wearable

• Wolf — travel diaries


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Hardware

• Garmin GPS model 35-LVS

• GeoStats data logger– 1 MPH recording limit


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Hardware

• Preliminary data collected in Atlanta Sep-Dec 2001

• Data currently being collected from multiple users in Zürich, Switzerland

Preliminary data—Atlanta, GA


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Software

• Preliminary implementation– finds points of possible significance

– creates probabilistic model of user’s movements•Markov model

– using model, simple queries are possible:•“The user is at home. Where will she go next?”

•“How likely is the user to visit the grocery store today?”


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Software

• Markov model– collection of nodes


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– transitions between nodes


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– each transition has a probability of occurring


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– each transition has a probability of occurring

– can also have self–

transitions


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• Our Markov model– nodes are significant

locations

– transitions are trips between those locations


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• Significance– how do we determine if a particular GPS

coordinate might have some meaning to the user?


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• Places– logged GPS

coordinates with more than time t of “resting time”


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• How to pick t ?


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• How to pick t ?– try lots of values

– graph number of places found for each value


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– but relationship is nearly linear!


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– but relationship is nearly linear!

– so we pick an arbitrary value: t = 10 minutes


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All data


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All data Only places,with t = 10m


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• Locations– problem: too many places

•GPS inaccuracy

•different exit points from buildings


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• Locations– problem: too many places

•GPS inaccuracy

•different exit points from buildings

– solution: cluster places to form locations•all places within a radius r of a particular place

form a single location


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Software

All data Only places,with t = 10m


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All data Only locationsOnly places,with t = 10m


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• How to pick radius r ?


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• How to pick radius r ?– too large value

• too few clusters• unrelated places

together

– too small value• too many clusters


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• How to pick radius r ?– too large value

• too few clusters• unrelated places

together

– too small value• too many clusters

• Solution:– try various values for r– find knee in graph


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• Clustering places into locations– pick one place (•)

– find all places within radius r (•)


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– find the mean of those places (x)


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– repeat with x as the new center


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– continue until the mean stops

changing


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– continue until the mean stops

changing

– start again with another place– repeat until no more places


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• Sublocations– problem: subsuming

smaller-scale paths


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• Sublocations– problem: subsuming

smaller-scale paths– solution: create

sublocations within larger clusters


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• How to determine if sublocations exist?


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• How to determine if sublocations exist?– use same knee &

graph algorithm on each location

– if no knee exists, not enough points to form sublocations


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• Sublocations can have multiple scales– Country level


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– State level


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– State level

– City level


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– State level

– City level

– Campus level


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• Prediction– each location gets a unique ID

•user may provide a unique name for each locationsuch as “home” or “work”

– replace each place in original list with ID•result: list of locations that were visited, in the

order that they were visited


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• For each location– count number of visits

to each other location


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to each other location– count total number of

visits to other locations


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– divide to get probability of transition


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– divide to get probability of transition

– result: Markov model for each location


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• People don’t move randomly!– 23 locations total, so chance of A→? = 1/22

= 4.5%

– measured ratio CRB→Home = 16/77 = 21%


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• Orders of Markov model– 1st order A → ?

•a given state’s transition probabilities only depend on that state

– 2nd order B → A → ?•a given state’s transition probabilities depend on

that state and the previous state

– and so on…


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Software

• First order predictions

4%

6%

6%

10%

13%

13%

21%

% Chance

3/77CRB → Taco Bell

5/77CRB → 10th/14th St.

5/77CRB → GA400

8/77CRB → Grocery store

10/77CRB → Jake’s Ice Cream

10/77CRB → Hardware store

16/77CRB → Home

ProbabilityMovement

••• ••

• •••

Random chance: 1/22 = 4.5%


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Software

7%

7%

14%

21%

21%

70%

% Chance

1/14Home → CRB → 10th/14th St.

1/14Home → CRB → GA400

2/14Home → CRB → Jake’s Ice Cream

3/14Home → CRB → Grocery store

3/14Home → CRB → Home

14/20Home → CRB

ProbabilityMovement

0%0/14Home → CRB → Hardware store

• Second order predictions

••• ••

• •••

Random chance: 1/22 = 4.5%


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Software

• How many orders to use?– sequence of 141 locations visited

– 23 total unique locations

86

82

73

56

Observed unique paths

137

138

139

140

Approx. expected unique paths

23 * 224 = 5,387,8884

23 * 223 = 244,9043

23 * 222 = 11,1322

23 * 221 = 5061

PermutationsOrder


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Future Work

• Collect more data– Georgia Tech students in Zürich & Atlanta

• Investigate other sensors for smaller scales– RF/IR beacons

• Consider privacy policies• Add time of day to Markov model

– predict when a user will leave as well as where they’re going


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Future Work

• Schedule “sharpness”– always on time = important ?– example: work at 8AM vs. grocery store

• Speed of model update vs. accuracy– new schedule for college students every term– weight new events more heavily?

•how to avoid unduly weighting one–time trips?•use confidence intervals to determine schedule

changes


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Future Work

• Real–time update of models– currently, data is post–processed

– need full wearable computers for real–time

• User interface– visualize location model

– allow user to influence model

• Favor trading implementation


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Thank You

Questions?

Learning Significant Locations and Predicting User ...Learning Significant Locations and Predicting...

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