[Geowanking] Predicting destination based on GPS History - looking for references
edward.vielmetti at gmail.com
Wed Sep 7 10:15:16 PDT 2011
Here's a 2011 thesis
and the title page and abstract
Predicting Future Locations and Arrival Times of Individuals
Ingrid E. Burbey
This work has two objectives: a) to predict people's future locations,
and b) to predict when they will be at given locations. Current
location-based applications react to the user‘s current location. The
progression from location-awareness to location-prediction can enable
the next generation of proactive, context-predicting applications.
Existing location-prediction algorithms predict someone‘s next
location. In contrast, this dissertation predicts someone‘s future
locations. Existing algorithms use a sequence of locations and predict
the next location in the sequence. This dissertation incorporates
temporal information as timestamps in order to predict someone‘s
location at any time in the future. Sequence predictors based on
Markov models have been shown to be effective predictors of someone's
next location. This dissertation applies a Markov model to
two-dimensional, timestamped location information to predict future
This dissertation also predicts when someone will be at a given
location. These predictions can support presence or understanding
co-workers‘ routines. Predicting the times that someone is going to be
at a given location is a very different and more difficult problem
than predicting where someone will be at a given time. A
location-prediction application may predict one or two key locations
for a given time, while there could be hundreds of correct predictions
for times of the day that someone will be in a given location. The
approach used in this dissertation, a heuristic model loosely based on
Market Basket Analysis, is the first to predict when someone will
arrive at any given location.
The models are applied to sparse, WiFi mobility data collected on PDAs
given to 275 college freshmen. The location-prediction model predicts
future locations with 78-91% accuracy. The temporal-prediction model
achieves 33-39% accuracy. If a tolerance of plus/minus twenty minutes
is allowed, the prediction rates rise to 77%-91%.
This dissertation shows the characteristics of the timestamped,
location data which lead to the highest number of correct predictions.
The best data cover large portions of the day, with less than three
locations for any given timestamp.
On Wed, Sep 7, 2011 at 1:01 PM, Rich Gibson <rich.gibson at gmail.com> wrote:
> Hi All,
> I think I remember an MIT project where students and faculty were tracked,
> and after
> 30 days the system was able to predict the destination of the lab rats, er,
> and faculty, based on their location.
> I seem to remember that faculty could be predicted with >80% success,
> students a
> bit late.
> Does anyone have a reference for this, and/or for other work on the subject?
> Geowanking mailing list
> Geowanking at geowanking.org
Edward Vielmetti +1 734 330 2465
edward.vielmetti at gmail.com
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