[Thomas Carson](slides)
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Transcript [Thomas Carson](slides)
More Accurate Bus Prediction
› Allows Passengers to find alternate forms of
transportation
Do this with energy efficiency in mind
Don’t use any high level permissions
Microphone – Record Sound
Cell Signal – Determine Location
Accelerometer - Determine Bus or Train
Query User – Looks for Bus arrival time by
indicating bus route and stop
Sharing User – Contributes mobile sensing
information to the backend server
› Information includes – a collected cell sequence
from nearby cell towers, sound and
accelerometer data to make sure the user is on
a bus
Backend Server – Processes data from
sharing users and give information to
querying users
Maintains a database of sequences for
cell tower IDs for the different Bus routes
Sound detection
Accelerometer Readings
Sequence Matching
After running an
Algorithm the Server
determines which
route has the best
score and that determines what bus the
sharing user is on
After all data is uploaded
and each bus is
determined where it is
Any querying user will
be able to get data on
where the bus is and
approximate arrival time.
No Users on a Bus
› Causes bus times to be reported wrong
Overlapped Routes
› The Server will sometimes misinterpret a route